abundance and diversity of total and nitrifying...
TRANSCRIPT
FACULTY OF AGRICULTURAL SCIENCES
Institute of Agricultural Sciences in the Tropics (Hans-Ruthenberg Institute)
University of Hohenheim
Field: Global Food Security
Prof. Dr. Georg Cadisch
Dissertation
Submitted in fulfillment of the requirements for the degree
"Doktor der Agrarwissenschaften"
(Dr.sc.agr. / Ph.D. in Agricultural Sciences)
to the
Faculty of Agricultural Sciences
presented by
Esther Kathini Muema
Kitui, Kenya, 2015
Abundance and diversity of total and nitrifying prokaryotes as influenced
by biochemical quality of organic inputs, mineral nitrogen fertilizer and
soil texture in tropical agro-ecosystems
This thesis was accepted as a doctoral dissertation in fulfillment of the requirements for the
degree "Doktor der Agrarwissenschaften” by the Faculty of Agricultural Sciences at University
of Hohenheim on October 7, 2015.
Date of oral examination: December 21, 2015
Examination Committee
Supervisor and Reviewer Prof. Dr. Georg Cadisch
Co-Reviewer Prof. Dr. Ellen Kandeler
Additional Examiner Prof. Dr. Bernard Vanlauwe
Head of the Committee Prof. Dr. Jens Wünsche
Co-supervisor
PD Dr. Frank Rasche
This work is dedicated to my departed father. Your final words before your last breath are
still very fresh in my mind. God has and will continue to honor your wishes towards my
education, career and life.
i
Acknowledgements
Spoken or written words are so shallow and limited to express the substance of the heart.
Nevertheless, this journey for the last 4 years and 7 months considering the time I spent for the
language course would not have been possible without you God. This road was characterized
by rocks, storms and strong waves. However, your great strength upon me all through can only
be likened to a palm tree by the sea shore whereby strong waves and winds bend it flat even
for several days but afterwards stands so straight upright without evidence that such incidents
had ever occurred. Such episodes have built my faith in you and I will forever be grateful. You
deserve all the Glory!
The University of Hohenheim situated in the south of Stuttgart is a great place to study soil
microbial ecology under the umbrella of Agricultural Sciences which is considered the top
department of Agronomy in Germany. More importantly, I have been very fortunate to pursue
my PhD studies surrounded by an inspiring group of upcoming and accomplished scientists,
who define the Cadisch research group. I admit it is impossible to acknowledge all individuals
who in one way or another contributed to the completion of this thesis, but I would like to
acknowledge some key individuals and institutions for their significant contributions.
First I thank Prof. Dr. Georg Cadisch and PD Dr. Frank Rasche for accepting to mentor and
walk with me during my PhD research. The great discussions, immense support and your
patience gave me the motivation to carry on. Thank you for believing in me and investing your
time and resources on me. Frank, I humbly appreciate your support and understanding even
when everything seemed impossible. I thank Prof. Dr. Ellen Kandeler for accepting to serve in
my defense committee. Thank you all the co-authors in the presented manuscripts for your
great team spirit, particularly Prof. Dr. Bernard Vanlauwe due to your busy schedule. I am
grateful to Leonard Mounde for keenly reading through my chapters and to Hannes Karwat,
Christian Brandt and Judith Zimmermann for helping with the translation of the abstract in
German.
Thank you Carolin Röhl for ensuring smooth laboratory analyses. In the same regard, I thank
Prof. Dr. Torsten Müller, Reza Mirzaeitalarposhti, Helene Ochott, Charlotte Haake and the
entire lab of crop science for allowing me to access your facilities and technical assistance. I
also thank Prof. Dr. Ludwig Hölzle (Institute of Environmental and Animal Hygiene and
Veterinary Medicine, University of Hohenheim) for access to his ABI 3130 Genetic Analyzer.
ii
I thank Prof. Dr. Hans-Peter Piepho, Dr. Juan Carlos Bayas and Dr. Joseph Ogutu (Department
of Bioinformatics, University of Hohenheim) as well as Andrew Sila of ICRAF Kenya, for
their support in statistical analysis. Special thanks go to Ms. Gabriele Kirche of 380a, Dr.
Brigitte Kranz of Food Security Center (FSC), Hohenheim and all the coordinators of section
“Africa ST32” of German Academic Exchange Service (DAAD) office for support with all the
administrative issues without which my stay in Germany won’t be possible. I thank the office
of the International students and the Welcome Center team for providing important logistical
support during my studies.
I am grateful to Dr. Monica Mucheru, of Kenyatta University (Kenya), and the field technicians
Kiragu and Mureithi for the management of the field trials and assistance during soil samplings.
I thank Evonne Oyugi, Mercy Nyambura and the technical staff at International Center for
Tropical Agriculture (CIAT) and World Agroforestry Centre (ICRAF) Kenya for their support
in soil analyses. At this point I would like to thank my scientific mentors Prof. Dr. J. R.
Okalebo, Dr. Owuoche, Dr. Wasike and Dr. Didier Lesueur wherever you are. Although the
greatest risk in life is to avoid taking risk at all, your great encouragement drove me to taking
this great challenge of pursuing my PhD.
This work was supported by a PhD scholarship grant by German Academic Exchange Service
(DAAD) as well as field operations and laboratory expenses finances by DAAD with funds of
the Federal Ministry of Economic Cooperation and Development (BMZ) of Germany and by
the foundation fiat panis Foundation (Ulm, Germany) through the Food Security Center (FSC)
of the University of Hohenheim in Germany.
My special thanks go to Dr. Nicholas Musyoka (Nick). You believed in me, gave me moral
support, encouraged me, provided me your shoulders to cry on whenever I was in wilderness,
and your thrill to see me accomplish a victor gave me the stamina to face the next challenge.
Finally, I wish to thank my mum, grand mum and siblings. Thank you for constant prayers,
best wishes and understanding. My special girlfriend Esther Kimeu, God brought us together
for a reason. Thank you for accepting to be part of my family and willfully and tirelessly took
my family responsibilities whenever I called upon you. In this respect, Kennedy and Timothy,
I am grateful for your immense support. My studies would not have been complete without
such a great people around me: Mary, Geraldine, Elizabeth, Lucy, Yvvone, Alpha, Chepkesis,
Jayne Binnot, Kitolo, Arisoa, Cecile Thonar, Cucu, May, Nimo and Jan among many others
who provided me a life outside the science.
iii
Table of contents
Acknowledgements .................................................................................................................................. i
Table of contents .................................................................................................................................... iii
Acronyms and abbreviations ................................................................................................................. vii
Chapter 1 General introduction .......................................................................................................... 1
1.2. Case studies of integrated soil fertility management approach ........................................................ 2
1.2.1. Long-term trials in Africa ......................................................................................................... 2
1.2.2. Soil organic matter (SOM) trials in Kenya and Zimbabwe ...................................................... 3
1.3. Dynamics of microbial communities as shaped by SOM ................................................................ 5
1.4. Environmental factors affecting N-cycling microbial communities ................................................ 9
1.4:1. Nutrient substrates .................................................................................................................... 9
1.4.2. Soil pH .................................................................................................................................... 10
1.4.3. Soil moisture and temperature ................................................................................................ 10
1.4.4. Soil texture .............................................................................................................................. 11
1.5. Molecular approaches to assess microbial communities ............................................................... 12
1.5.1. Whole community analysis approaches .................................................................................. 14
1.5.2 Partial community analysis approaches ................................................................................... 14
1.5.2.1 Principle of qPCR ............................................................................................................. 14
1.5.2.2 Principle of terminal restriction fragment length polymorphism (TRFLP) ...................... 15
1.5.2.3 Denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel
electrophoresis (TGGE) ................................................................................................................ 16
1.5.3. Limitations and proposed measures of molecular community analysis techniques ................ 17
1.6. Focus of current work and link to food security ............................................................................ 17
1.7. Problem statement and justification ............................................................................................... 18
1.8. Hypotheses ..................................................................................................................................... 20
1.9. Objectives ...................................................................................................................................... 21
1.9.1. Overall objective ..................................................................................................................... 21
1.9.2. Specific methodological objectives ........................................................................................ 21
1.10. Structure of the thesis ................................................................................................................... 22
Chapter 2 Response of ammonia oxidizing bacteria and archaea to biochemical quality of
organic inputs combined with mineral nitrogen fertilizer in an arable soil .................................. 23
2.1 Introduction ..................................................................................................................................... 24
iv
2.2 Materials and methods .................................................................................................................... 26
2.2.1 Site description and experimental design ................................................................................. 26
2.2.2 Soil sampling ........................................................................................................................... 27
2.2.3 Microbiological soil analysis ................................................................................................... 28
2.2.3.1 Soil DNA extraction.......................................................................................................... 28
2.2.3.2 Microbial abundance ......................................................................................................... 28
2.2.3.3 Microbial community composition ................................................................................... 29
2.2.4 Geochemical properties............................................................................................................ 29
2.2.5 Statistical analysis .................................................................................................................... 32
2.3 Results ............................................................................................................................................. 36
2.3.1 Microbial abundance ................................................................................................................ 36
2.3.2 Microbial community composition .......................................................................................... 36
2.3.3 Geochemical properties............................................................................................................ 38
2.3.4 Regression analysis .................................................................................................................. 40
2.4 Discussion ....................................................................................................................................... 45
2.4.1 Organic input quality effects on microbial communities ......................................................... 45
2.4.2 Mineral N fertilizer depresses microbial abundance except AOB ........................................... 47
2.5 Conclusions and recommendations ................................................................................................. 49
Chapter 3 Dynamics of bacterial and archaeal amoA gene abundance after additions of organic
inputs combined with mineral nitrogen to an agricultural soil ...................................................... 51
3.1 Introduction ..................................................................................................................................... 52
3.2 Materials and methods .................................................................................................................... 54
3.2.1 Site description and field experiment design ........................................................................... 54
3.2.2 Soil sampling ........................................................................................................................... 56
3.2.3 Gene abundance analysis ......................................................................................................... 57
3.2.4 Soil physico-chemical analyses ............................................................................................... 58
3.2.5 Statistical analysis .................................................................................................................... 58
3.3 Results ............................................................................................................................................. 59
3.3.1 Microbial abundance dynamics in response to organic input quality ...................................... 59
3.3.2 Effect of mineral N fertilizer on microbial communities ......................................................... 64
3.3.3 Effect of input quality and mineral N fertilizer on soil physico-chemical properties .............. 64
3.3.4 Correlation between soil physico-chemical properties and microbial abundance ................... 69
3.4 Discussion ....................................................................................................................................... 69
3.4.1 AOB and AOA abundance respond conversely to organic input quality ................................ 69
3.4.2 Effect of mineral N fertilizer on dynamics of soil microbial communities ............................. 72
v
3.5 Conclusions ..................................................................................................................................... 73
3.6 Supplement ..................................................................................................................................... 74
Chapter 4 Soil texture interrelations with organic inputs quality contrastingly influence
abundance and composition of ammonia-oxidizing prokaryotes in a tropical arable soil ........... 77
4.2 Materials and methods .................................................................................................................... 80
4.2.1 Sites description and experimental design ............................................................................... 80
4.2.2 Soil sampling ........................................................................................................................... 81
4.2.3 Microbiological soil analysis ................................................................................................... 81
4.2.3.1 Soil DNA extraction.......................................................................................................... 81
4.2.3.2 Microbial abundance ......................................................................................................... 82
4.2.3.3 Microbial community composition ................................................................................... 82
4.2.4 Physico-soil chemical analyses ................................................................................................ 85
4.2.5 Statistical analysis .................................................................................................................... 85
4.3 Results ............................................................................................................................................. 87
4.3.1 Dynamics of abundance of microbial communities ................................................................. 87
4.3.2 Dynamics of microbial community composition ..................................................................... 92
4.3.3 Effects of biochemical quality of inputs on soil chemical properties ...................................... 96
4.3.4 Linear correlations and regression analyses between soil chemical properties and soil
microbial communities .................................................................................................................... 100
4.4 Discussion ..................................................................................................................................... 102
4.4.1 Response of microbial gene abundance and community composition to texture .................. 102
4.4.2 Response of AOB to interrelations of soil texture and organic input quality ........................ 102
4.4.3 Soil texture exert stronger effect on AOA than organic input quality ................................... 104
4.5 Conclusions ................................................................................................................................... 105
4.6 Supplement ................................................................................................................................... 106
Chapter 5 General discussion .......................................................................................................... 107
5.1 Is continuous application of organic resources to boost SOM a solution to soil degradation in the
tropics? ................................................................................................................................................ 108
5.2 Implications of quality of organic resources and mineral N fertilizer on dynamics of microbial
communities in a clayey soil ............................................................................................................... 109
5.3 Implications of interrelations of soil texture and quality of organic residues on abundance and
composition of microbial communities ............................................................................................... 111
5.4 Microbial dynamics versus crop performance as shaped by quality of residues and mineral N
fertilizer ............................................................................................................................................... 112
5.5 Conclusions ................................................................................................................................... 114
vi
5.6 Future directions ........................................................................................................................... 115
Summary ............................................................................................................................................ 117
Zusammenfassung............................................................................................................................. 120
References .......................................................................................................................................... 124
vii
Acronyms and abbreviations
ABI = Applied Biosystems
AIC = Akaike information criterion
ANOSIM = Analysis of similarity
ANOVA = Analysis of variance
AOA = Ammonia-oxidizing archaea
AOB = Ammonia-oxidizing bacteria
a.s.l = Above the sea level
°C = Degree celcius
C = Carbon
CA = California, USA
CaNH4NO3 = Calcium ammonium nitrate
CAP = Canonical Analysis of Principal coordinates
CC = Calliandra calothyrsus
CIAT = International Center for Tropical Agriculture
cm = Centimetre
COR = Correlations
DAAD = Deutscher Akademischer Austausch Dienst
DISTLIM = Distance-based linear models
DNA = Deoxyribonucleic acid
EC30 = Young plants growth stage
EC60 = Flowering stage.
FAM = Carboxyfluorescein dye
FAO = Food and Agriculture Organization of the United Nations
FastDNA TM SPIN = A Kit that contains all the components needed for isolation of soil DNA.
FSC = Food Security Centre
GDP = Gross Domestic Product
GE = General Electric
GLIMMIX = generalized linear mixed models
GmbH = Gesellschaft mit beschränkter Haftung
ha = Hactare
HEX = Carboxyhexachlorofluorescein dye
HIDI = The Highly Deionized Formamide
HWEC = Hot water extractable carbon
HWEN = Hot water extractable nitrogen
ICRAF = World Agroforestry Centre
LR = Long rains
LSMEANS = Least square means
K = Potassium
Km = Kilometre
m = metre
MA = Massachusetts, USA
MC = Moisture content
viii
MP = Milan Panic the Founder, President and Chief Executive of MP Biomedicals
N = Nitrogen
n.s = Not significant
NC = North Carolina, USA
NEB = New England Biolabs
NH3- = Ammonia
NH4+ = Ammonium
NO3- = Nitrates
Nt = Total nitrogen
P = Phosphorus
PDIFF = Pairwise differences
PERMANOVA = Permutation multivariate analysis of variance
PRB = Population Reference Bureau
PROC = Procedure
QIAGEN = A trade name for a company that provides kits with all components needed for
the isolation of genetic materials.
qPCR = Quantitative Polymerase Chain Reaction
Q-Q = Quantile plots
REG = Regression
RNA = Ribosomal nucleic acid
ROX = carboxy-x-rhodamine dye
SAS = Statistical Analysis Software
SED = Standard error of the difference
SOC = Soil organic carbon
SR = Short rains
SOM = Soil organic matter
SSA = Sub-Saharan Africa
SYBR Green = An asymmetrical cyanine dye (Fluorescent DNA binding dye)
TAMRA = tetramethylrhodine azide
T4 gene 32 protein = A nucleic acid binding protein that destabilizes the DNA helical
structure
TC = Total carbon
TD = Tithonia diversifolia
TRFs = Terminal Restriction Fragments
TRFLP = Terminal Restriction Fragment Length Polymorphism
USA = United States of America
U = Units
WI = Winsconsin, USA
ZM = Zea mays
1
Chapter 1 General introduction
1.1. Persistent soil nutrients mining and proposed measures
In Africa, particularly in Sub-Saharan Africa (SSA) agriculture and agro-industry sectors form
the backbone of economic development. Agriculture contributes to more than 25 % of the
Gross Domestic Product (GDP) and is rated the main source of income and employment of at
least 65 % of Africa’s population (Henao and Baanante, 2006; World Bank, 2012).
Consequently, it is directly linked to soil nutrients mining (Sanchez, 1997) through crops
harvesting, leaching, erosion or volatilization in form of greenhouse gases (Hai et al., 2009;
Kibunja, 2007). Soil degradation has therefore significantly and continuously suppressed soil
productivity in SSA region.
The two viable options of increasing crop production are either through intensive production
per unit of land or by increasing the area under cultivation (Henao and Baanante, 2006).
Nevertheless, SSA has experienced a steady population pressure with up to 2.5 to 4 % growth
per annum (Population Reference Bureau (PRB), 2015; World Bank, 2012). This rapid
population growth has constrained the arable land as more land has been used for settlements
and infrastructure. Consequently, shifting cultivation which was traditionally used to replenish
soil fertility is no longer possible. This strategy used to leave the land under cultivation fallow
for an adequate period to regain fertility and opening virgin areas. Unfortunately, the
disappearance of shifting cultivation has not been matched with sufficient use of inorganic and
organic (FAO, 2012) fertilizers to maintain soil quality and productivity. Substantial increases
in use of inorganic fertilizers was viewed a viable means to increase crop production per unit
of land (Minot and Benson, 2009). However, according to Vanlauwe et al. (2010), inorganic
fertilizers should be considered in the framework of integrated soil fertility management
(ISFM) approach or in a holistic cropping management approach like conservation agriculture
(CA) (FAO, 2008). Nevertheless, this option has been faced with limited quantities of inorganic
fertilizers available to small scale farmers, i.e., very low intensity (< 10 nutrients kg ha-1) of
use, and slow rates of growth in fertilizer consumption (FAO, 2012). This is mainly due to high
poverty levels among smallholder farmers in SSA and high costs of inorganic fertilizers while
organic fertilizers face challenges of competing uses like animal fodder, but inadequate manure
production in return (Vanlauwe et al., 2010).
2
Use of organic residues or manures to improve soil fertility is facilitated by the activities of
soil microbial communities (Rasche and Cadisch, 2013). There is therefore a need to
understand the basic processes underlying the decomposition and mineralization of organic
residues by soil microbial communities. This is because microbial mediated processes avail
nutrients held in organic to inorganic forms like ammonium (NH4+) and nitrates (NO3
-) that are
accessible to plants eventually improving soil productivity (Zhang et al., 2013).
1.2. Case studies of integrated soil fertility management approach
1.2.1. Long-term trials in Africa
Integrated soil fertility management (ISFM) strategy is defined as application of inorganic
fertilizers and organic inputs with improved germplasm combined with knowledge of how to
adapt this practice to local conditions (Vanlauwe et al., 2010). The aim is to maximize
agronomic use efficiency of the applied nutrients to improve crop productivity. This approach
relies heavily on immense contribution of organic resources classification for smallholder
farmers earlier developed by Palm et al. (2001b). Accordingly, organic resources were
classified either as high, intermediate or low quality. Further details including the management
of the mentioned categories are found in section 1.3 of this thesis. In this respect, a long-term
(20 years) trial in Burkina-Faso showed a significant improvement in sorghum grain yield and
soil organic matter (SOM) accumulation upon combination of low quality sorghum straw
residue with urea fertilizer over sole use of either input (Mando et al., 2005). This was attributed
to a better synchrony of supply and demand of nutrients by combination of mineral fertilizer
and low quality inputs. Low quality inputs slowly decompose and mineralize availing nutrients
to plants during demand. This improves crop productivity and prevents nutrient losses via
leaching (Chivenge et al., 2009; Gentile et al., 2009; Mtambanengwe et al., 2006). Similar
observations on crop grain yields were reported in Niger in a 17 years old trial as well as in
Kenya, Kabete after 18 years and Chuka after 6 years (Bationo et al., 2012). These positive
interaction effects were also attributed to provision of other nutrients like P and K by residue
inputs influencing the uptake of N (Partey et al., 2013; Sanchez and Jama, 2002). In addition,
organic inputs influence other physical, chemical or biological properties like soil aggregation
that might have positively impacted on soil productivity (Fonte et al., 2009; Kunlanit et al.,
2014; Puttaso et al., 2011).
3
1.2.2. Soil organic matter (SOM) trials in Kenya and Zimbabwe
In 2002 ISFM trials were set up in Kenya (Embu and Machanga sites, Fig. 1.1) and in
Zimbabwe in two contrasting environments (clayey and sandy soils). The goal of the trials was
to investigate the effect of different qualities of organic resources and combination with mineral
N fertilizer on management of N availability and losses, soil stabilization and carbon
sequestration as well as crop productivity (Chivenge et al., 2009; Gentile et al., 2009;
Mtambanengwe et al., 2006). In Zimbabwe, Mtambanengwe et al. (2006) observed up to seven-
fold increases in maize grain yields upon the combination of low quality inputs and mineral N
compared to sole use of organic residues in the nutrient depleted sandy soil during the first year
of the trial. The sandy clay-loam soil on the other hand did not show significant treatment
differences in maize grain yields. This could be attributed to the high nutrient levels of the
sandy clay-loam compared to the sandy soil (Mtambanegwe et al., 2006), as well as high N
losses via leaching associated with high quality inputs masking treatments effects on crop
yields (Gentile et al., 2009; Mapfumo et al., 2007).
At the Kenyan site, high quality organic inputs (i.e., Tithonia diversifolia) and combinations of
intermediate (Calliandra calothyrsus) and low quality (Zea mays) inputs with mineral N
averaged across ten seasons increased maize productivity (Chivenge et al., 2009). These
findings were attributed to the influence of soil N dynamics by residues quality during the
growing season, both when applied alone and in combination with mineral N fertilizer.
Combination of low quality maize stover residue with mineral fertilizer reduced N losses and
resulted in positive interactive effects on crop N uptake and high maize yields (Chivenge et al.,
2009; Gentile et al., 2009; Mapfumo et al., 2007; Mtambanengwe et al., 2006). The reduced N
leaching was as a result of induced N immobilization by microbes, followed by slow
decomposition and mineralization later in the growing season which coincided with plants
demand, thus affecting productivity (Gentile et al., 2009). Provision of inorganic soil nutrients
as demonstrated by increased crop productivity was associated with accumulated SOM through
continued application of organic inputs. SOM has effects on the physico-chemical functions of
the soil since it promotes good soil structure which in turn improves the tilth, aeration, retention
of soil moisture as well as increased buffering and exchange capacity of soils (Vanlauwe et al.,
2001b). For example, Zea mays stover with low N content increased soil aggregate stability,
soil organic C and total soil N (Chivenge et al., 2011). Biologically, SOM provide resource
substrates for the metabolic activities of soil microbial communities which include
decomposition and mineralization of organic inputs. In turn, microbial communities influence
4
soil structure by their by-products and nutrient fluxes (Lucas, 2013; Six et al., 2004). These
multiple benefits to soil resource derived from SOM via the incorporation of organic residues
through the activities of microbial communities, coupled with the development of advanced
molecular tools, raised the need to understand the interactions between soil microbial
communities and organic resources and implications to sustainable soil resource management.
Figure 1.1: Map of the study sites, Embu (0˚ 30' S, 37˚ 30' E) and Machanga (0˚ 47' S, 37˚
40' E) in Kenya, GIS ICRAF (2007).
5
1.3. Dynamics of microbial communities as shaped by SOM
Figure 1.2: Schematic representation of the flow of nitrogen through the environment.
Microbial communities are key in the cycle, providing different forms of nitrogen compounds.
Source: https://en.wikipedia.org/wiki/Nitrogen_cycle
Generally, soil microbial communities are catalysts of global C and N cycles during
decomposition and mineralization of SOM (Fontaine et al., 2003). This influences fluxes of
nutrients like N (Fig. 1.2), SOC as well as greenhouse gases like CO2, N2O and CH4. SOM
therefore act as a habitat for microbial communities which in the event contribute to binding
of soil particles into aggregates through the release of their by-products (Chivenge et al., 2011;
Fonte et al., 2009; Gentile et al., 2011a, 2011b; Kunlanit et al., 2014). As a result, dynamics of
soil microbial communities during SOM decomposition and mineralization has widely been
studied. For example, Kamaa et al. (2011) showed contrasting variations in diversity of fungal
and bacterial communities between a fallow and farm yard manure treated plots in the tropics.
Accordingly, the fallow showed lower fungal but higher bacterial diversity but vice versa for
the farm yard manure. Regulation of fungal community abundance and activities by quality of
Proteolysis
6
organic residues in a sandy soil in tropics was reported by Kamolmanit et al. (2013). In their
findings, the most recalcitrant (high polyphenol content) residues depressed fungal community
abundance but promoted the activity of specific functional groups as marked by increased
polyphenol oxidase activity. Focusing on N-cycle, Magdalena and Stefania (2011), Mrkonjic
et al. (2008), Okur et al. (2009), Rasche et al. (2014) and Sakurai et al. (2007) studied the initial
step of N mineralization on different types of organic residues by assessing the dynamics and
functions of bacterial proteolytic communities. These authors demonstrated that type of organic
resources which intern influence the fluxes of organic C and N in the soil control the dynamics
and activities of bacterial communities. Hai et al. (2009) and Hallin et al. (2009) demonstrated
manure treatment as the main determinant of nitrogen fixation (nifH), nitrate reduction (narG)
as well as denitrification (nirK, nirS and nosZ) genes abundance compared to cereals straw
treatments. Additionally, Chèneby et al. (2010) reported promotion of abundance and alteration
of diversity of nitrate reducing communities (narG and napA) genes with use of wheat, rape
and alfalfa residues compared to no input treatment. Several other studies focused on
nitrification process (Di et al., 2010, 2009; Levičnik-Höfferle et al., 2012; Rasche et al., 2011;
Strauss et al., 2014; Wessén et al., 2010).
Figure 1.3: Autotrophic ammonia oxidation during nitrification. Ammonia-oxidizing
organisms convert ammonia to nitrite through hydroxylamine using ammonia monooxygenase
(AMO) and hydroxylamine oxidoreductase (HAO). Autotrophic nitrite oxidizers subsequently
use the enzyme nitrite oxidoreductase (NOR) to convert nitrite to nitrate, which can be
assimilated, lost through leaching or subjected to denitrification processes. In anaerobic
environments, ammonia can be converted to molecular nitrogen by the ‘anammox’ process by
several enzymatic steps (represented by dashed arrows).
Source: Hyman and Arp (1992), Nicol and Schleper (2006).
7
Emphasizing on ammonia-oxidizing bacterial (AOB) and archaeal (AOA) communities by
focusing on ammonia monooxygenase (AMO) (Fig.1.3), these authors showed distinct
responses of AOB and AOA to organically mineralized and fertilizer N. SOM therefore
regulates biogeochemical soil processes via the activities of microbial communities with
consequent impact on soil productivity. The complexity and variability of SOM influence
biogeochemical processes by modifying specific microbial communities. Its wide range
includes green manures, crop and plant residues as well as animal remains (Fig.1.2). These
make it a substance of different qualities due to variations in biochemical compounds like
cellulose, hemicelluloses, starches, proteins, lipids and polyphenols consequently influencing
rates of decomposition (Kunlanit et al., 2014; Kononova, 1966; Palm et al., 2001b; Partey et
al., 2013). The biochemical properties mainly the organic N (proteins), lignin and polyphenol
contents as well as the recently reported cellulose create interspecific differences and play a
major role in regulation of N dynamics in soils (Kunlanit et al., 2014; Palm et al., 2001b; Partey
et al., 2013). In this regard, a decision guide manual was developed for organic N management
in tropical agro-ecosystems (Palm et al., 2001b). The organic materials were differentiated into
four quality classes (I to IV) (Fig.1.4). Resources containing high lignin and polyphenol
contents (intermediate high quality) classified as class II, induce N limitation through the
formation of protein-polyphenol complexes (PPCs) which are resistant to microbial
decomposition (Kraus et al., 2003; Millar and Baggs, 2004; Talbot and Finzi, 2008). In
contrast, high quality resources with high N content but low lignin and polyphenol contents
(class I) are rapidly decomposed and mineralized. Organic nutrients thus released, particularly
NH4+ and NO3
- are either available for plants uptake, lost through leaching in the case of NO3-
or immobilized by the microbes (Kaewpradit et al., 2008; Kanerva et al., 2006).
8
Figure 1.4: Decision tree for biomass transfer of plant materials for soil fertility management.
Source: Palm et al. (2001b).
Vanlauwe et al. (2005) and Kaewpradit et al. (2008) confirmed the potential of Tithonia
diversifolia and groundnut residues classified as high quality resource to rapidly release
mineralized N particularly at the beginning of the vegetation period. An intermediate-high
quality resource, (Calliandra calothyrsus), was proved to slowly release N during
decomposition, which is however available for plants uptake over a vegetative growth season
including the succeeding crop (Cadisch et al., 1996). Such a delayed N release is associated
with formation of PPCs which inhibit immediate microbial decomposition and consequent
mineralization (Millar and Baggs, 2004; Mutabaruka et al., 2007; Partey et al., 2013; Talbot
and Finzi, 2008). Maize stover is composed of low N, lignin and polyphenol contents
(Chivenge et al., 2011; Gentile et al., 2011; Shephered et al., 2003; Tian et al., 1995), falling
under intermediate-low quality group (Palm et al., 2001b). Intermediate-low quality materials
have N immobilization similar to the intermediate-high quality organic materials. However,
maize stover was recently revealed to increase microbial abundance of some specific groups
compared to Calliandra calothyrsus (Muema et al., 2015). This observation was associated
with the high cellulose content in maize stover which is accessible to microbial decomposition
(Kunlanit et al., 2014; Puttaso et al., 2011). Consequently, maize stover increased soil
aggregate stability, soil organic carbon (SOC) and N compared to the intermediate-high quality
inputs (Chivenge et al., 2011). Therefore, high quality inputs were proposed to be used as
substitutes, while the intermediate and low quality resources as complements to mineral
fertilizers (Partey et al., 2013; Palm et al., 2001a, 2001b). From this overview, it is clear that
Yes
No
% N
> 2.5
Lignin <15%
Phenols <4%
Lignin <15%
Phenols <4%
Yes
No
Yes
No
High quality (I)
Incorporate directly
Intermediate high quality (II)
Mix with N fertilizer or high
quality organic matter
Intermediate low quality (III)
Mix with N fertilizer or add
to compost
Low quality (IV)
Apply at the soil surface
9
quality of organic resources and mineral fertilizers have impacts on soil resources in several
aspects. Firstly, by improving soil productivity as proved by increased crop grain yields
(Chivenge et al., 2009; Mtambanegwe et al., 2006; Partey et al., 2013). Secondly, by
influencing soil C and N dynamics (Chivenge et al. 2011; Gentile et al., 2011a, 2011b) and
finally these inputs influence dynamics of microbial communities (Chèneby et al., 2010; Hai
et al., 2009; Kamolmanit et al., 2013; Rasche et al., 2014). However, the effects of integration
of quality of organic inputs, mineral N fertilizer, seasonality and soil texture on nitrogen cycle
prokaryotes in arable tropical conditions particularly in SSA is not fully understood.
1.4. Environmental factors affecting N-cycling microbial communities
1.4:1. Nutrient substrates
N-cycling microbial communities have revealed contrasting responses to N addition in soils
depending on N source, whether organic or inorganic. For example, Hallin et al. (2009)
demonstrated a reduction of narG, nirK, nirS and nosZ genes abundance by ammonium
fertilizer compared to cattle manure. N fixation (nifH) and organic N mineralization (chiA)
genes showed contrasting responses to N fertilizer availability. Specifically, these genes
showed high competitiveness when N was limiting, increasing in abundance but their
abundance decreased when N was high due to reduced competitiveness (Zhang et al., 2013).
Chèneby et al. (2010) reported a promotion of abundance and changes in diversity of nitrate-
reducing community (narG and napA) genes with N addition from wheat, rape and alfalfa
residues compared to no input treatment. In another study, variations in the amount of organic
N content in different organic residues had different impacts on fungal community abundance
in a sandy soil. Accordingly, high and low quality inputs showed significantly lower fungal
abundance compared to the intermediate quality inputs (Kamolmanit et al., 2013). Several other
studies have also reported promotion of AOB by high levels of NH3 (de Gannes et al. 2014; Jia
and Conrad, 2009; Levičnik-Höfferle et al. 2012; Schleper, 2010; Zhalnina et al., 2012; Zhang
et al., 2013). On the other hand, mixed reactions exist concerning the influence of NH4+ on the
growth or activities of AOA. For example, Di et al. (2009) and Tourna et al. (2010) reported
inhibition of AOA nitrification under high NH4+ concentrations. On the contrary, Treusch et
al. (2005) reported growth and activity of AOA in soil to be promoted by high NH4+
concentration. Moreover, substrate concentrations (i.e., low, medium or high) levels of NH3
were reported not to influence the growth or activity of AOA (Di et al., 2009; Ke and Lu, 2012;
Stopnišek et al, 2010; Verhamme et al., 2011). These contradictions were associated with a
10
high affinity of AOA for NH3/NH4+ (de Gannes et al. 2014; Levičnik-Höfferle et al. 2012;
Martens-Habbena and Stahl, 2011; Martens-Habbena et al., 2009). This observation suggests
that AOA can obtain their energy even under low levels of substrate, masking the effects of
NH3 concentrations. Sources of NH3 or NH4+ have also been reported to greatly influence both
AOB and AOA (Jia and Conrad, 2009; Zhang et al., 2010). In this respect, while AOB are
highly promoted by mineral N, AOA have been shown to thrive best on organically mineralized
NH4+ (Di et al., 2009; Hofferle et al., 2010; Ke and Lu, 2012; Stopnisek et al., 2010; Verhamme
et al., 2011).
1.4.2. Soil pH
The influence of soil pH on N-cycling communities is linked to NH3 availability in soils (Nicol
et al., 2008; Zhalnina et al., 2012). Reduction in soil pH was found to reduce the abundance of
nifH, chiA, nirS, nirS and nosZ genes (Hallin et al., 2009; Zhang et al., 2013). AOA have high
affinity for NH3 and that is why they dominate acidic soils compared to AOB (Gubry-Rangin
et al., 2010; Martens-Habbena et al., 2009; Nicol et al., 2008). As for AOB, their low affinity
for NH3 explains why their abundance and activities are low in acidic soils as NH3 is limiting
(de Boer and Kowalchuk, 2001; Zhalnina et al., 2012). Previous studies have reported increased
abundance and activity of AOA in acidic (pH = 3.6 - 4.0) soils (Leininger et al., 2006; Gubry-
Rangin et al., 2010; He et al., 2007; Zhang et al., 2011; Isobe et al., 2012), while increase in
pH increased AOB abundance and its activities (Che et al., 2015; Nicol et al., 2008). Moreover,
Shen et al. (2008) reported a positive correlation for AOB with soil pH. On the other hand, Yao
et al. (2011) revealed AOA driven nitrification compared to AOB in the acidic tea grown soils.
Nevertheless, AOA have also been reported in alkaline soils (Bates et al., 2011; Bru et al.,
2011; Shen et al., 2008; Wessén et al., 2010; Zhang et al., 2010). However, it is clear that AOB
dominate and are more active in alkaline conditions while AOA dominate and drive
nitrification in acidic conditions.
1.4.3. Soil moisture and temperature
Soil moisture content and temperature are critical in N-cycle processes like SOM
decomposition and mineralization (Belnap, 2001; Kirschbaum, 1995; Norton and Stark, 2011;
Zheng et al., 2000; Zhang et al., 2013). Soil water is a medium upon which nutrients are
transported while temperature is critical in controlling microbial enzymatic activities. Heavy
precipitation can cause leaching of NO3- which could lead to a decrease in denitrifiers.
However, wetting of soils could potentially buffer acidification effects of N additions and
promote denitrifiers. Moreover, the buffered soil pH combined with increased watering can
11
create an anaerobic environment which could stimulate denitrifiers (Zhang et al., 2013). Soil
moisture content was reported to control the activities of proteolytic communities i.e., neutral
metalloproteases and serine proteases (npr; sub) genes with accelerated activities recorded in
autumn compared to summer (Mrkonjic et al., 2009, 2008). Decreased potential activity of
proteolytic activity with reduction in soil moisture content was also observed by Hofmockel et
al. (2010) and Sardans and Penuela (2005). Rasche et al. (2011) revealed dynamics of N-cycle
prokaryotes groups as shaped by seasonality in a beech forest in temperate regions. For
example, AOB increased with increased moisture content while AOA increased with decreased
temperatures. Similarly, AOB and AOA were reported to bear different responses to different
water filled pore spaces (WFPS) and temperature with AOB being much more sensitive than
AOA (Avrahami and Bohannan, 2007; Gleeson et al., 2010; Szukics et al., 2010). Accordingly,
65 % was the approximate optimum WFPS for AOB while AOA remained rather stable over a
wide range from 25 to 95 %. In addition, AOB were abundant and active at 20-25 ºC while
AOA were active in low temperatures (< 15 ºC) but reduced at temperatures greater than 25
ºC. Szukics et al. (2012) reported high abundance of AOA in dry soil condition (i.e., 40 %)
WFPS compared to wet soil condition (70 %) WFPS. Angel et al. (2010) found differences in
composition of archaeal and bacterial communities between arid, semiarid and Mediterranean
regions which were associated to differences in precipitation.
1.4.4. Soil texture
Soil texture is a physical property described by proportions of different solid fractions (clay,
silt, sand and organic matter) and provide microbial habitats (Garbeva et al., 2004). Variations
in these fractions particularly the organic matter is responsible for the fertility and productivity
of soil resource as an ecosystem (Chivenge et al., 2009; Mtambanengwe et al., 2006; Rasche
and Cadisch, 2013). This is due to the ability of SOM to provide nutrients to crops and
microorganisms as well as regulation of soil aggregation (Bossuyt et al., 2001; Chivenge et al.,
2011; Fonte et al., 2009; Gentile et al., 2011a, 2011b). For example, high abundance and
activities of proteolytic genes were measured in the clayey soil associated with high organic C
and N compared to the sandy soil in southern Germany (Mrkonjic et al., 2009, 2008). In
addition, proportions of mineral fractions regulate other factors like O2 levels. For instance,
high clay content has a likely hood of reducing O2 levels favoring AOA compared to AOB
(Morimoto et al., 2011). Evidence has shown that soil type is an important determinant of
microbial populations and composition in the bulk and rhizosphere soils. For example,
Gelsomino et al. (1999) showed distinct variations in patterns of bacterial profiles across 16
12
different types of soils of the Netherlands using denaturing gradient gel electrophoresis
technique (PCR-DGGE). These findings revealed soil type as a determinant of bacterial
structures and that similar soil types contained similar communities. Girvan et al. (2003)
observed soil type as the main driver of separation of total bacterial and functional communities
over management practices. Clayey soil texture promoted the composition of total bacterial
communities compared to soil organic amendments (Sessitsch et al., 2001). Rasche et al. (2014)
determined distinct differences in abundance and composition of total and proteolytic bacteria
between clayey and sandy textured soils. In addition, soil texture dependent responses of total
and ammonia-oxidizing prokaryotes were reported by Musyoki et al. (2015) and Neumann et
al. (2013). Clayey soils have a high SOC background and a strong soil structure particularly
protecting freshly incorporated SOM from microbial access whereas sandy soils are generally
nutrient limiting and have a coarse soil structure which exhibit less protection to SOM ( Kögel-
Knabner et al., 2008; Puttaso et al., 2011). These soil type specific properties result in distinct
responses of specific microbial communities (chapter four of this thesis, Musyoki et al., 2015;
Rasche et al., 2014).
1.5. Molecular approaches to assess microbial communities
Culture-based approaches are vital in microbial ecology but are limited since they present <
1% of the total number of microbial species present in any given sample (Hugenholtz, 2002).
In addition, cultivable microbial species are numerically hardly abundant or functionally
significant in environments they are cultured (Rastogi and Sani, 2011). This limitation has been
overcome by development of molecular techniques which are able to assess the uncultured but
viable microbial species based on direct isolation and analysis of nucleic acids, proteins and
lipids from environmental samples including soil and water sediments. These approaches are
able to reveal community structures, functions and dynamics of microbial communities and
help to understand their interactions with biotic and abiotic environmental factors (Rastogi and
Sani, 2011). Overall, these techniques are classified into two main categories following their
capacity to portray microbial structure, function and diversity as follows: 1) whole community
analyses approaches and 2) partial community analyses approaches (Fig. 1.5).
13
Figure 1.5: Culture-independent molecular toolbox to characterize the structural and
functional diversity of microorganisms in the environment. Source: Rastogi and Sani (2011).
Environmental samples e.g., soil, water and sediments
Extraction of DNA/RNA/Proteins/Lipids
Molecular based methods
Whole community analysis methods Partial community analysis methods
Proteogenomics
DNA-DNA reassociation
Whole genome sequencing
G+C fractionation
Metagenomics
Metaproteomics
Metatranscriptomics
Genetic fingerprinting techniques such as ARDRA,
SSCP, TRFLP, DGGE, RISA, LH-PCR, RAPD
Clone library method
QPCR (real-time PCR)
FISH, dot-blot
hybridization Microbial lipid analysis
DNA microarrays
Microautodiography and isotope array
DNA/RNA Stable isotope probing
CARD-FISH, Rama-FISH,
NanoSIMS Functional diversity
Structural diversity
Protein diversity
Metabolic diversity
14
1.5.1. Whole community analysis approaches
These techniques are based on analysis of whole-genome of microbial communities present in
the total DNA extracted from an environmental sample (Rastogi and Sani, 2011). They thus
overcome the limitation of use of 16S rRNA genes (partial analysis techniques) which is highly
conserved and therefore has limited resolution at species and strain level (Konstantinidis et al.,
2006). PCR-based approaches also target single or few genes limiting the genetic information
contained in the isolated total DNA. Whole community molecular analyses approaches are
however more expensive compared to partial analysis techniques. Different approaches used
for whole community studies are shown in figure 1.5 and their descriptions are provided by
Rastogi and Sani (2011) and Singh et al. (2010).
1.5.2 Partial community analysis approaches
These techniques employ polymerase chain reaction (PCR)-based approaches such that
DNA/RNA isolated from environmental samples is used as a template for characterization of
microbes. Basically, PCR generated product is a mixture of microbial genes signatures present
in a sample including those viable but non-cultured (VBNC) proportion (Rastogi and Sani,
2011). The advantages of these methods are that the conserved region (16S rRNA) used for
PCR amplification is: i) present in all prokaryotes, ii) structurally and functionally conserved,
and iii) contain variable highly conserved regions (Hagenholz, 2002). Specific methods used
are shown in figure 1.5 and further descriptions can be retrieved from Rastogi and Sani (2011).
1.5.2.1 Principle of qPCR
Quantitative PCR (qPCR or real-time PCR) is a standard PCR with the advantage of detecting
the amount of DNA formed after each cycle with either fluorescent dyes or fluorescently-
tagged oligonucleotide probes. This technique is used to measure the abundance and expression
of taxonomic and functional gene markers (Bustin et al., 2005; Smith and Osborn, 2009). The
quantity of the detected DNA can either be an absolute number of copies or a relative amount
when normalized to DNA input or additional normalizing genes. Its procedure follows the
principle of PCR but its main feature is that, the amplified DNA is detected as a reaction
progresses in real time. Two common methods for detection of products in real-time PCR are:
i) non-specific fluorescent dyes that intercalate with any double stranded DNA (SYBR green-
based), and ii) sequence-specific DNA probes consisting of oligonucleotides that are labeled
with a fluorescent reporter which permits detection only after hybridization of the probe with
15
its complementary DNA target (probe-based) (Rastogi and Sani, 2011). QPCR technique is
robust, highly reproducible and sensitive.
1.5.2.2 Principle of terminal restriction fragment length polymorphism (TRFLP)
TRFLP is a rapid fingerprinting technique used for studying diversity, structure and dynamics
of microbial communities (Rastogi and Sani, 2011; Smaller et al., 2007). It is a method that
profiles mixed populations of a homologous amplicon which is a diverse sequence of a single
gene. Isolated DNA from environmental sample is amplified using fluorescent universal or
specific primers (Liu et al., 1997; Schütte et al., 2008). The amplified PCR products are
digested using restriction enzymes. Labelled terminal fragments are detected and sized by
capillary with automated DNA sequencer or using gel electrophoresis. One of the primers is
labeled at a 5’ terminus with a fluorescent dye, so that the terminal restriction fragments (TRFs)
of the digested amplicon can be detected and quantified by the automated DNA sequencer (Liu
et al., 1997; Schütte et al., 2008). From the DNA sequencer electropherograms are generated
(i.e., the visual profile of the community). Simultaneously, digital data in a table format is
generated whereby in principle, peak heights and areas are representative of community
abundance. In TRFLP technique, results are reproducible and it is a relatively faster way of
monitoring community dynamics which produces digital data that can be used for further
analyses e.g cluster analysis, calculation of diversity indices, and correlations with
environmental data like soil chemical properties. TRFLP data can also be linked to clone
libraries (Deutzmann et al., 2002). However, this technique is challenged with underestimation
of microbial populations through the following challenges;
i) Choice of primers. This is so because although primers may potentially amplify relatively
high microbial 16S rRNA gene sequences in Ribosomal Database Project (RDP), this does not
take into account that the sequence databases contain only a fraction of the surviving/extant
bacterial diversity. Labeling of one primer underestimates the microbial diversity in a sample
because different bacterial populations can share the same terminal restriction fragment length
for a particular enzyme combination. This problem can be reduced if the two primers are
labelled.
ii) Choice of restriction enzymes. Use of one restriction enzyme can underestimate the
microbial diversity because different bacterial populations can share the same terminal
restriction fragment length for a particular primer-enzyme combination. This can be resolved
by using more than one restriction enzyme to facilitate resolution of microbial population.
16
iii) Differences in migration speed of labeled DNA as a result of differences in molecular
weights of different labeling dyes (Pandey et al., 2007; Schütte et al., 2008). Different
molecular weight can modify the mass of individual DNA fragments which may lead to
differential migration. For example fluorescent dye like carboxyfluorescein (FAM) is lighter
than carboxyhexachlorofluorescein (HEX). This means that DNA labeled with FAM can
migrate faster than DNA labeled with HEX. Internal standard dyes such as tetramethylrhodine
(TAMRA) azide, carboxyl-x-rhodamine (ROX) also have different molecular weights. TRFs
from lighter labeling dyes could thus be underestimated since it is not easy to adjust for
differences as the magnitude of discrepancy is not constant across fragment sizes (Pandey et
al., 2007; Shütte et al., 2008).
1.5.2.3 Denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel
electrophoresis (TGGE)
For DGGE, DNA is amplified using primers for specific molecular marker (e.g., 16S rRNA
gene) and electrophoresed on a polyacrylamide gel containing a linear gradient mixture of
chemical denaturants (e.g., urea and formamide) (Muyzer et al., 1999, 1993). The same applies
for TGGE but instead a temperature rather than chemical gradient is used. Since fragment
sequence differences dictate the melting point of a sample DNA, both techniques are able to
separate DNA fragments of the same size but different sequences depending on their melting
points. They use a 5’- GC clamp (30-50 nucleotides) forward primer during the PCR step which
prevents two DNA strands from complete dissociation into single strands during
electrophoresis (Rastogi and Sani, 2011). Their disadvantages include: i) limited sequence
information (< 500 base pairs (bp)) obtained for phylogenetic analysis from DNA bands, ii)
possibility of different DNA fragments sharing similar melting points, iii) several varied DNA
fragments, which can be separated by polyacrylamide gel electrophoresis, and iv) sequence
heterogeneity between multiple rRNA eperons of single bacterium can lead to multiple bands
causing overestimation of the diversity. More fingerprinting techniques are discussed in
Rastogi and Sani (2011).
Studies that have compared TRFLP and DGGE techniques provide a consistent evidence that
both approaches give similar outputs (Enwall and Hallin, 2009; Smalla et al., 2007), suggesting
the two approaches to be alternatives. The choice of TRFLP approach for this study therefore
was based on the availability of the equipment at the laboratory, the large sample size and the
desire to broaden knowledge to other approaches as was already conversant with DGGE
17
technique. In my own experience however, DGGE demands extra care when particularly
handling the polyacrylamide gels as they are very fragile.
1.5.3. Limitations and proposed measures of molecular community analysis techniques
1) Biases during DNA extraction. According to Feinstein et al. (2009), this setback could
be overcome by isolation of DNA using several validated protocols and pooling it
together prior to PCR performance.
2) Biases encountered during PCR like inhibition of PCR by co-extracted humic acids.
Purification of DNA should be performed although it may lead to loss of DNA during
purification. Dilution of DNA can be applied although also interferes with PCR
efficiency.
3) Efficiency of hybridization and primers specificity can cause preferential amplification
of certain templates interfering with the quantitative assessment of microbial diversity.
4) Formation of PCR artifacts like primer dimers may also provide misleading results.
Therefore primer pairs that generate primer-dimers should be avoided. For the qPCR
technique, extensive optimization of primer concentrations especially with the SYBR
green fluorescent dye should be conducted to ensure that only the target product is
formed. In addition, melting curve (post-PCR dissociation) analysis should be carried
out to confirm that fluorescence signals generated are from the target templates but not
from non-specific amplifications.
1.6. Focus of current work and link to food security
The role of microbial communities in decomposition and mineralization processes underscores
their importance in nutrient recycling and geochemical processes which governs the overall
functioning and health of soils. This places a need to understand at a community level the
spatial and temporal variability of microbial populations in response to agricultural practices.
The emphasis of ISFM strategy particularly in SSA to increase soil productivity in terms of
grain yields (Vanlauwe et al., 2010) therefore laid the foundation for this work. This was due
to the use of organic inputs locally available to smallholder farmers in addition to inorganic
fertilizers to replenish soil nutrients. Importantly, N is one of the macronutrients whose
inadequate supply in soils limits crops growth and productivity (Ågren et al., 2012). Its adverse
effects have however, mainly been evident in the old weathered soils of the tropics (Bationo et
al., 2012). This drove my interest to understand at a community level the dynamics of nitrogen
cycle prokaryotes specifically those involved in the nitrification process, as this has not been
18
reported in Kenya. Nitrification is a two-step process in nitrogen cycle whereby ammonia
(NH3) is oxidized to nitrite (NO2-) and eventually nitrates (NO3
-) which are available to
microorganisms and plants (Petersen et al., 2012; Zhang et al., 2013). Nitrification is
catalystized by the activities of ammonia-oxidizing bacterial (AOB) and archaeal (AOA)
communities (Arp et al., 2002). Ammonia oxidizers’ critical role in N regulation has been
underscored (Di et al., 2010, 2009; Erguder et al., 2009; Jia and Conrad, 2009; Leininger et al.,
2006; Musyoki et al., 2015; Prosser and Nicol, 2008, 2012; Schleper, 2010; Schleper et al.,
2005; Zhalnina et al., 2012; Zhang et al., 2010). In addition, reports have shown the associated
improved soil productivity through increases in grain yields upon incorporation of organic
resources either solely or in combination with inorganic fertilizers particularly in nutrients
limited soils (Chivenge et al., 2009; Mapfumo et al., 2007; Mtambanengwe et al., 2006). On
the basis of the prior introduced medium to long-term SOM trials established in Kenya in 2002,
the abundance and composition of total and ammonia-oxidizing prokaryotes governing the
decomposition and mineralization of SOM were assayed in two contrasting environments.
1.7. Problem statement and justification
Soil degradation remains a topical issue which negatively affects food security particularly in
SSA where population pressure is constantly increasing while the land factor remains constant
or even reducing with increased infrastructure. With regard to the two proposed mechanisms
of improving soil productivity, i.e. increasing the land area under cultivation or by intensive
production from the already existing arable land, the latter has widely been adopted. From the
presented overview, the significance of the ISFM strategy with specific emphasis on the role
of the biochemical quality of organic resources in improving crop productivity has been
confirmed (Bationo et al., 2012; Chivenge et al., 2009; Gentile et al., 2009; Mando et al., 2005;
Mapfumo et al., 2007; Mtambanengwe et al., 2006; Partey et al., 2013). Moreover, its role in
improving soil aggregation, chemical and biological soil properties by controlling dynamics of
SOC and SON was reported (Bossuyt et al., 2001; Chivenge et al., 2011; Fonte et al., 2009;
Gentile et al., 2011a, 2011b; Kunlanit et al., 2014; Puttaso et al., 2001, 2013; Samahadthai et
al., 2010; Six et al., 2006). The biological properties as indicators of soil quality have been
extended to studying the dynamics of soil microbial communities (Hai et al., 2009; Jia and
Conrad, 2009; Musyoki et al., 2015; Wessén et al., 2010; Rasche et al., 2014). In addition,
abiotic factors that affect populations and activities of soil microbial communities like soil pH,
soil moisture content and temperature as well as soil texture have been discussed (Che et al.,
19
2015; Girvan et al., 2003; Szukics et al., 2012, 2010; Yao et a., 2011; Zhalnina et al., 2012).
Nevertheless, most of field reports on soil processes through the study of the dynamics of soil
microorganisms at a community level as shaped by the biochemical quality of organic inputs
and inorganic fertilizers particularly in tropical arable systems have been based on one occasion
or single season soil sampling (Hai et al., 2009; Rasche et al., 2014). Since the tropics are
characterized by erratic rainfall between seasons within and across years, it is still not well
understood, and has not been reported in the framework of ISFM strategy in SSA, how
seasonality shapes the effects of quality of organic resources on dynamics of microbial
communities at the community level. Hence, consideration of two consecutive years (2012 and
2013) during long rains seasons in SOM field trials (that were established in Kenya in 2002 in
the framework of ISFM strategy) was a unique factor in this study. Soil samplings were
conducted prior to the incorporation of soil inputs and after soil amendments at distinct maize
growth stages in two contrasting soil textures (clayey and sandy). This was vital to distinguish
the effect of the buildup of organic matter over time from that of freshly incorporated organic
inputs on dynamics of soil microbial communities. Accordingly, generated data were used to
firstly understand how 10 years of continuous application of different quality organic resources,
to restore the depleted SOM and provide nutrients to crops, shaped the abundance and diversity
of nitrogen cycling prokaryotes. Secondly, the dynamics of microbial communities in response
to interactions between seasonality and quality of organic inputs given the competition of
nutrients from the crops, were elucidated. The third component considered the interrelations of
soil texture and quality of organic residues on dynamics of soil microbial communities. This
knowledge is critical in assessing soil quality as well as understanding the dynamics and
functions of the soil ecosystem. Such information can be used to sensitize key stakeholders like
farmers and particularly policy makers to incorporate the aspect of organic fertilizers in
decision making for site-adapted management practices which would ensure improved food
production.
20
1.8. Hypotheses
1. Continuous application of high quality organic inputs, due to their high content of
organic N whose decomposition releases high amounts of NH4+ as opposed to low
(limited N) and intermediate (high polyphenol and lignin contents induced N limitation)
quality inputs, promotes the abundance and diversity of total bacterial and archaeal as
well as ammonia-oxidizing bacterial (AOB) and archaeal (AOA) communities.
2. Combination of mineral N fertilizer with low and intermediate quality inputs promotes
the abundance and diversity of total and nitrifying prokaryotes because of its
compensation effect for the limiting N. This is facilitated through the direct provision
of NH4+, a source of energy for AOB and AOA, as well as a source of N for N-
demanding processes of total microbial communities (e.g., protein synthesis).
3. Increased microbial abundance is expected under high quality inputs at young plants
growth stage (EC30) due to the faster decomposition of organic N leading to a high
availability of NH4+ as opposed to intermediate quality inputs with high polyphenol
and lignin contents and low quality inputs with low organic N contents inducing NH4+
limitation. On the other hand, a reduced microbial abundance at EC60 with high quality
inputs was presumed due to negligible remnants of decomposable organic N as
compared to the intermediate and low quality inputs with their delayed and thus gradual
decomposition of N.
4. A clayey soil, regardless of quality of organic inputs, promotes the abundance and
diversity of total and ammonia-oxidizing communities due to its native nutrient rich
status. This is specifically because of the availability of organic N derived from the
strong soil organic matter background, a source of substrates for soil microorganisms.
In addition, the hypothesized high microbial abundance is supported by clay-sized soil
particles providing a large surface area for microbial and residue attachment. A sandy
soil, on the other hand, promotes the abundance of microbial communities and alters
their composition upon application of high quality inputs early in the growing season.
This is due to the high content of accessible decomposable organic N in high quality
inputs availing NH4+. Similar responses of microbial communities were expected for
the intermediate and low quality inputs but later in the season as a result of their gradual
N decomposition. Such expectations were anticipated since sandy soils are natively
nutrient limited and characterized by a coarse soil texture rendering less protection of
organic inputs to microbial access.
21
1.9. Objectives
1.9.1. Overall objective
The overall goal of this work was to understand the impact of long and short-term use
of organic and inorganic inputs on the population dynamics of total and functional
microbial communities involved in the nitrification process in two contrasting
environments in the tropics.
1.9.2. Specific methodological objectives
1. To study the effects of continuous application of different quality organic inputs and
their combinations with mineral N on the DNA-based functional potential
abundance and diversity of total bacteria and archaea as well as AOB and AOA after
10 years.
2. To study the dynamics of total bacterial and archaeal as well as AOB and AOA
DNA-based functional potential abundance as shaped by the biochemical quality of
organic inputs combined with mineral N fertilizer at young growth (EC30) and
flowering (EC60) stages of maize during two long rain seasons.
3. To determine the response of abundance (DNA-based functional potential
abundance) and diversity of total bacterial and archaeal as well as AOB and AOA
communities to the interactions between quality of organic inputs and soil texture
(clayey and sandy).
22
1.10. Structure of the thesis
This thesis is composed of five chapters. Chapter 1 provides a general overview of soil fertility
depletion in Africa and suggested options which are linked to microbial mediated processes on
SOM turnover. It introduces organic inputs quality and inorganic soil inputs which are
components of integrated soil fertility management technology (ISFM). Chapter 2 reports on
impacts of long-term (10 years) application of biochemically contrasting organic resources and
their combination with mineral nitrogen (N) fertilizer on the abundance and composition of
total and ammonia-oxidizing prokaryotes using quantitative polymerase chain reaction (qPCR)
and terminal restriction fragment length polymorphism (TRFLP) techniques respectively in a
clayey soil in Kenya. Chapter 3 discusses dynamics of the abundance of total and nitrifying
prokaryotes as influenced by quality of organic inputs and mineral N fertilizer at distinct crop
growth stages over two consecutive seasons in a clayey soil in Kenya. This was vital to
understand the short-term effects of freshly incorporated organic inputs on dynamics of
microbial decomposer communities. It was also crucial to understand the input quality
seasonality related effects on microbial communities. Chapter 4 investigates the effects of
interrelations of soil type (clayey and sandy textures) and quality of organic residues on the
abundance and composition of ammonia-oxidizing bacteria (AOB) and archaea (AOA). Such
knowledge is useful to develop site adapted organic management options as tropics are
characterized by varied and distinct soil types. Chapter 5 forms the general discussion. Here,
an overview and feedback on implications of long and short-term application of organic and
inorganic inputs in consideration of distinct soil textures is reported on the following aspects;
i) soil quality and sustainability, ii) dynamics of soil microbial communities and soil
productivity, and iii) future directions. This thesis proceeds with a summary in English and a
German translation with additional information in the appendices.
23
Chapter 2 Response of ammonia oxidizing bacteria and archaea to biochemical quality
of organic inputs combined with mineral nitrogen fertilizer in an arable soil1
Esther K. Muemaa, Georg Cadischa, Carolin Rohla, Bernard Vanlauweb, Frank Raschea
aInstitute of Plant Production and Agroecology in the Tropics and Subtropics, University of
Hohenheim, Stuttgart, Germany
bInternational Institute of Tropical Agriculture (IITA)-Kenya, P.O. Box 30709, Nairobi, Kenya
1 This chapter has been published as:
Muema, E.K., Cadisch G., Röhl, C., Vanlauwe, B., Rasche, F., 2015. Response of ammonia-
oxidizing bacteria and archaea to biochemical quality of organic inputs combined with mineral
nitrogen fertilizer in an arable soil. Applied Soil Ecology. 95, 128–139.
24
Abstract
There exists a considerable knowledge gap about the effect of biochemical quality of organic
inputs and their combination with inorganic N on abundance and community composition of
ammonia-oxidizing archaea (AOA) and bacteria (AOB). Here, we investigated in a Humic
Nitisol of 10-year old field experiment in Kenya the effect of contrasting organic inputs (i.e.,
Tithonia diversifolia (TD; C/N ratio: 13, lignin: 8.9 %; polyphenols: 1.7 %), Calliandra
calothyrsus (CC; 13; 13; 9.4) and Zea mays (ZM; 59; 5.4; 1.2)); rate of 4 Mg C ha-1 year-1)
combined with mineral N fertilizer (120 kg CaNH4NO3 ha-1 growing season-1) on amoA gene-
based abundance (i.e., functional potential) and community composition of AOB and AOA.
AOB abundance was significantly lower in CC and ZM compared to TD, whereas AOA
abundance was significantly lower in CC compared to ZM and TD. This reduction was
attributed to a considerable N stress induced by limited organic N availability in ZM and
polyphenol-protein complexes in CC. High abundance of AOA under ZM was attributed to
their affinity to ammonium under N limiting conditions. Abundance shifts matched observed
community composition differences between TD versus ZM (AOB) as well as TD versus ZM
and CC (AOA). Mineral N irrespective of organic input type depressed abundance of AOA,
but not AOB. This implied utilization of ammonium from fertilizer and organic N by AOB,
while AOA mainly utilized ammonium from organic N. Our findings suggested input type
dependent effects on AOB/AOA abundance and community composition, but influence of
other factors such as soil type, seasonality and crop growth stages remain uncertain. These
factors should not only be studied on basis of the functional potential of ammonia oxidizing
prokaryotes as given in this study, but also by rRNA analyses to capture the active proportion
of existent AOB and AOA.
Keywords: Ammonia-oxidizing bacteria; Ammonia-oxidizing archaea; Abundance;
Community composition; Biochemical organic input quality; Mineral nitrogen fertilizer.
2.1 Introduction
Combined and particularly site-adapted use of mineral fertilizers and organic inputs (i.e., crop
residues as well as prunings from shrubs and trees) has been recommended for soil fertility and
crop productivity enhancement in resource limited smallholder tropical cropping systems
(Partey et al., 2013; Rasche and Cadisch, 2013; Vanlauwe et al., 2010). To maximize the
beneficial effects of organic inputs on crop nutrition and soil organic matter build-up, an
25
explicit decision support system for smallholder farmers was developed by Palm et al. (2001b).
This defines high quality organic inputs (rich in organic nitrogen (N) (> 2.5 %), but low in
polyphenols (< 4 %)) to be applied primarily as substitution of mineral fertilizers, while
intermediate (high contents of organic N (> 2.5 %) and polyphenols (> 4 %)) and low quality
(low contents of organic N (< 2.5 %) and polyphenols (< 4 %)) inputs should be used as
complement to mineral fertilization.
On basis of this decision support system, long-term field experiments were initiated in Kenya
and Zimbabwe in 2002 to evaluate the effect of contrasting biochemical quality organic inputs
with and without mineral N fertilizer applications on crop performance and soil carbon (C) and
N dynamics (Chivenge et al., 2009; Gentile et al., 2011a; Mtambanengwe et al., 2006). At the
Kenyan site, located in the central highlands, high quality organic inputs (i.e., Tithonia
diversifolia) and combination of intermediate (Calliandra calothyrsus) and low quality (Zea
mays) inputs with mineral N improved soil fertility and increased maize production (Chivenge
et al., 2009). Moreover, Z. mays stover with low N content increased soil aggregate stability,
soil organic C and total soil N compared to the polyphenol rich inputs of C. calothyrsus in the
short-term. However, this positive effect was inversed when Z. mays inputs were combined
with mineral N fertilizer (Chivenge et al., 2011).
In contrast to this knowledge gain on geochemical dynamics and crop performance, the effect
of long-term use of biochemically contrasting organic inputs in combination with mineral N
fertilizers on soil microbial communities remain uncertain. A critical knowledge gap within the
frame of these long-term field experiments is left, although it is acknowledged that organic
input quality is critically regulating microbial decomposition and mineralization including
nitrification (Partey et al., 2013; Wardle and Giller, 1996) and proteolysis (Rasche et al., 2014).
Consequently, soil microbial communities have been recognized to control the synchrony of
crop available nutrients including ammonium (NH4+) and nitrate (NO3
-) with crop demand
(Vanlauwe et al., 2010).
Microbial nitrification plays a central role in the terrestrial N cycle through ammonia- oxidizing
bacteria (AOB) and (AOA) using a α-subunit of monooxygenase (amoA gene) as catalyzing
enzyme (Hyman and Arp, 1992; Nicol and Schleper, 2006). Extensive studies have shown that
prokaryotic ammonia oxidation is partially controlled by organic inputs and mineral
fertilization (Wessén et al., 2010; Wu et al., 2011). In this sense, de Gannes et al. (2014) and
Levičnik-Höfferle et al. (2012) evidenced distinct community differences between AOA and
AOB with respect to contrasting N availabilities. In their studies, it was reported that AOA
adapted successfully to low NH4+ environments, while AOB showed opposite tendencies.
26
Furthermore, Jia and Conrad (2009) reported the dominance of AOB over AOA in soils
receiving N fertilizer, while an opposite effect was assessed by Zhang et al. (2010). Wessén et
al. (2010) showed consistently higher AOA abundance than AOB with use of straw and peat
inputs in combination with mineral N soil amendments although AOB was higher in peat input
without N fertilizer. It is worthwhile emphasizing that these long-term studies were restricted
to temperate environments and most of them apart from Wessén et al. (2010) focused on the
sole use of either organic inputs or mineral fertilizer. Contrastingly, AOB and AOA community
dynamics in tropical soils under agricultural use have been hitherto underestimated with respect
to their exposure to both biochemically contrasting organic inputs and mineral N fertilizer.
The primary aim of this study was to assess the effects of ten years continuous application of
biochemically contrasting organic inputs in combination with mineral N fertilizer on the
functional potential (DNA-based abundance quantification) of AOB and AOA and their
respective community composition (terminal restriction fragment length polymorphism
analyses) in a tropical soil under agricultural use. We hypothesized a higher AOB and AOA
abundance along with altered community composition with the use of high quality inputs due
to a higher accessibility of organic C and N substrates as opposed to low quality inputs.
Furthermore, with respect to the combination of mineral N with low and intermediate quality
organic inputs, we hypothesized a higher AOB abundance than AOA in response to an
enhanced degradation of organic inputs to alleviate N stress.
2.2 Materials and methods
2.2.1 Site description and experimental design
The experimental site was located in Embu (0˚ 30' S, 37˚ 30' E; 1380 m above sea level) in the
central highlands of Kenya (130 km northeast of Nairobi). The site has an annual mean
temperature of 20 °C and a mean annual rainfall of 1200 mm. Rainfall is bimodal with long
rains received from mid-March to June and short rains from mid-October to December. The
soil is defined as a Humic Nitisol (FAO, 2006) dominated by Kaolinite minerals derived from
basic volcanic rocks. The texture of the topsoil layer (0-15 cm) was characterized by 17 %
sand, 18 % silt and 65 % clay and contained 29.4 g kg-1 organic C, 2.7 g kg-1 total N and a pH
of 5.8 (H2O) at start of experiment in 2002 (Gentile et al., 2009).
The soil organic matter (SOM) long-term field experiment was established to study primarily
the effect of continuous annual application of biochemically contrasting organic inputs with
and without inorganic mineral N fertilizer on crop performance as well as soil C and N
27
dynamics (Chivenge et al., 2011, 2009; Gentile et al., 2011a). The experiment was laid out in
a split plot design with three replicates per treatment with organic inputs as main plots (size,
12 m x 5 m) and mineral N fertilizer as sub-plots (size, 6 m x 5 m) located in the organic input
plots. Three organic input types were considered in this study: high quality Tithonia diversifolia
(C/N ratio: 13, Lignin: 8.9 %; Polyphenols: 1.7 %), intermediate quality Calliandra calothyrsus
(13; 13 %; 9.4 %) and low quality Zea mays (59; 5.4 %; 1.2 %) inputs (Gentile et al., 2011b).
At the onset of long rains in each year, organic inputs (leaves, petioles and small branches for
C. calothyrsus in addition to stems for T. diversifolia and Z. mays stover) were collected and
analyzed for dry matter and total C and N content. Dry matter and total C data were then used
to determine the amount of each organic material to be applied amounting to 4 Mg C ha-1 a-1
(Chivenge et al., 2009; Gentile et al., 2011a). Prior to maize sowing, organic inputs were
chopped into small pieces, broadcast and hand-incorporated at a soil depth of 0-15 cm
(Chivenge et al., 2009; Gentile et al., 2011a). In the sub-plots of each plot, mineral N fertilizer
was applied as calcium ammonium nitrate (CaNH4NO3) at a rate of 0 and 120 kg N ha-1
growing season-1 (Chivenge et al., 2009; Gentile et al., 2011a, 2011b). One third of the mineral
N fertilizer was applied 3 weeks after sowing of Z. mays (test crop) and the remainder 8 weeks
later by broadcasting and incorporating into soil (Chivenge et al., 2009). Mineral N fertilizer
was recommended as a complement for low and intermediate quality organic inputs to
compensate the presumed N limitation (Palm et al., 2001b; Partey et al., 2013). For treatment
comparison purposes in the presented study, the mineral N fertilizer was also combined with
the high quality organic input. All plots received a blanket basal application of 60 kg P ha-1
season-1 and 60 kg K ha-1 season-1 before sowing. Further details of the field experiment set-up
can be retrieved from Chivenge et al. (2009) and Gentile et al. (2009).
2.2.2 Soil sampling
In March 2012, at onset of long rains and prior to incorporation of fresh organic inputs, soil
samples were collected from the plots designated for the crop (Zea mays) (Gentile et al., 2011a).
This sampling time point allowed us to assess the targeted long-term effects of organic and
mineral inputs on alteration of the functional potential (abundance) and community
composition of AOB and AOA, while avoiding interference of short-term microbial
community dynamics due to initialized decomposition and mineralization of fresh inputs. The
following 8 treatments (each replicated three times) were considered: 4 treatments without
mineral N fertilizer including the no input control (CON) and 3 organic input treatments (i.e.,
C. calothyrsus (CC), Z. mays (ZM) and T. diversifolia (TD). The other 4 treatments were their
28
respective counterparts receiving mineral N fertilizer. In each plot, 10 soil sub-samples were
randomly obtained at a depth of 0-15 cm using a soil auger. These 10 sub-samples were bulked
to 1 composite and representative sample per plot sieved (2-mm mesh) and stored at 4 °C. In
total, 24 soil samples were obtained. Prior to analysis, each composite soil sample was split
into 3 proportions. A fresh sample was used for immediate extraction of NH4+ and NO3
-. The
second proportion was air-dried for analysis of pH, hot water extractable C and N (HWEC/N),
total C (TC) and total N (Nt). The third proportion was freeze-dried to be shipped to Germany
for microbial community analyses.
2.2.3 Microbiological soil analysis
2.2.3.1 Soil DNA extraction
Total soil DNA was isolated from 0.5 g of each freeze dried soil sample using the FastDNATM
SPIN Kit for Soil (MP Biomedicals, Solon, Ohio, USA) according to the manufacturer’s
instructions. Quality of isolated DNA was checked on 1.5 % (w/v) agarose gels. DNA was
quantified photometrically (NanoDrop™ 2000/2000c spectrophotometer, Thermo Fisher
Scientific, Waltham, MA, USA). For each isolated total DNA sample, 2 consistent NanoDrop
readings, whose variation was less than 5 %, were considered for subsequent determination of
DNA concentration. Quantified DNA was stored at -20°C until further analysis.
2.2.3.2 Microbial abundance
For DNA-based quantification of target genes (total bacteria and archaea (16S rRNA gene),
ammonia-oxidizing bacteria (bacterial amoA gene; AOB) and archaea (archaeal amoA gene;
AOA)), plasmid standards were prepared (Table 2.1), purified (Invisorb Fragment CleanUp
kit, Stratec Molecular GmbH, Berlin, Germany) and quantified according to Rasche et al.
(2011). The qPCRs (3 analytical replicates per DNA sample) were conducted in a 25 µl reaction
cocktail containing 12.5 µl Power SYBR green master mix (Applied Biosystems, Foster City,
CA, USA), 0.4 µM of each primer (Table 2.1), 0.25 µl T4 gene 32 protein (500 μg ml-1, MP
Biomedicals) and 5 ng DNA template (except for AOB gene (10 ng)) from the prior prepared
DNA working stock of 5 ng µl-1 concentration. The optimal dilution of DNA extracts was tested
before to compensate any reaction inhibition by humic acids co-extracted during DNA isolation
(data not shown). Reactions were run on a StepOne Plus Real-time PCR detection system
(Applied Biosystems) starting with an initial denaturation step at 95 °C for 10 min, followed
by amplification cycles specific for each target gene (Table 2.1). Melting curve analysis of
amplicons was conducted to ensure that fluorescence signals originated from specific
29
amplicons and not from primer dimers or other artifacts. Gene copy numbers and reaction
efficiencies (total bacteria 106 %, total archaea 71%, AOB 96 %, AOA 63 %) were calculated
using StepOne Software version 2.2.2 (Applied Biosystems) and presented as gram of dry soil.
2.2.3.3 Microbial community composition
For terminal restriction fragment length polymorphism (TRFLP) analysis, the total (16S rRNA
gene) and ammonia-oxidizing (amoA genes) prokaryotic communities were amplified as
described previously (Rasche et al., 2011) (Table 2.1). All forward primers were labeled with
6-carboxyflourescein at their 5’ ends. Amplicons (both 16S rRNA genes, AOA) were purified
(Invisorb Fragment CleanUp kit) prior to digestion. For AOB, 3 replicate PCRs were pooled,
precipitated (Rasche et al., 2006b), and purified (QIAGEN® Gel Extraction Kit, Hilden,
Germany). Two hundred ng of each purified amplicon were digested with 5 U AluI (New
England Biolabs (NEB), Ipswich, USA) for bacterial and archaeal 16S rRNA gene amplicons,
while a 5 U combination of AluI and RsaI (NEB) was used for AOB and AOA. Reactions were
incubated at 37 °C for 4 hours (bacterial 16S rRNA genes, AOB, AOA) and overnight (archaeal
16S rRNA genes). The latter was necessary to gain a more efficient digestion. Digested
products were purified (Sephadex G-50, GE Healthcare Biosciences, Waukesha, WI, USA)
(Rasche et al., 2006a) and an aliquot of 2 µl was mixed with 17.75 µl HiDi formamide (Applied
Biosystems) and 0.25 µl internal 500 ROX™ size standard (Applied Biosystems). Labeled
terminal restriction fragments (TRFs) were denatured at 95°C for 3 min, chilled on ice and
detected on an ABI 3130 automatic DNA sequencer (Applied Biosystems). Peak Scanner™
software package (version 1.0, Applied Biosystems) was used to compare relative lengths of
TRFs with the internal size standard and to compile electropherograms into numeric data set,
in which fragment length and peak height >50 fluorescence units were used for profile
comparison. TRFLP profiles used for statistical analyses were normalized according to Dunbar
et al. (2001).
2.2.4 Geochemical properties
Soil pH values were measured in water suspensions at a solid: liquid ratio of 1:2.5. Hot water
extractable C (HWEC) and N (HWEN) analyses were determined according to Schulz (2004)
as well as Schulz and Korschens (1998). C content of hot water extracts was analyzed using
potassium dichromate in sulfuric acid milieu (Multi N/C analyzer, Analytik Jena, Jena,
Germany), while HWEN was analyzed by a modified Kjeldahl digestion. Total C (TC) and
total N (Nt) of soils were quantified by dry combustion (Flash EA 1112 Elemental Analyzer,
30
Thermo Fisher Scientific). Ammonium (NH4+) and nitrate (NO3
-) were calorimetrically
measured spectrophotometrically on field fresh soils (ICRAF, 1999).
31
Table 2.1: Description of primer sets, PCR ingredients and amplification details used for quantitative PCR (qPCR) and TRFLP analyses.
Target gene Primer (reference) qPCR TRFLP
All bacteria Eub338f (Lane, 1991)
Eub518r (Muyzer et al., 1993)
95 °C 5 min; 40 cycles: 95 °C 30s,
55 °C 35s, 72 °C 45s
8f (Weisburg et al., 1991)
1520r (Edwards et al. 1989)
95 °C 5 min; 40 cycles: 95 °C 1
min, 58 °C 30s, 72 °C 1 min; 72
°C 10 min
All archaea Ar109f (Lueders and Friedrich, 2000)
Ar912r (Lueders and Friedrich, 2000)
95 °C 5 min; 40 cycles: 95 °C 30s,
52 °C 35s, 72 °C 45s, 78 °C 20s
95 °C 5 min; 35 cycles: 95 °C 1
min, 52 °C 30s, 72 °C 1 min; 72
°C 10 min
Ammonia oxidizing
bacteria (AOB)
AmoA-1f (Rotthauwe et al., 1997)
AmoA-2r (Rotthauwe et al., 1997)
95 °C 5 min; 45 cycles: 95 °C 30s,
57 °C 45s, 72 °C 45s, 78 °C 20s
95°C 5 min; 40 cycles: 94°C 30s,
53°C 30s, 72°C 1 min; 72°C 10
min
Ammonia oxidizing
archaea (AOA)
Arch-amoAf (Francis et al., 2005)
Arch-amoAr (Francis et al., 2005)
95 °C 5 min; 45 cycles: 95 °C 30s,
53 °C 45s, 72 °C 45s, 78 °C 20s
94 °C 5 min; 35 cycles: 94 °C 30s,
53 °C 45s, 72 °C 10 min; 72 °C 1
min
32
2.2.5 Statistical analysis
Analyses of variance (ANOVA) were conducted using Statistical Analysis Software program
SAS (SAS Institute, 2014). For the abundance data of the four studied genes, a generalized
linear mixed model with a negative binomial distribution error and a log link function was
used. The model was fitted by restricted log pseudo-likelihood with expansion about the
solution for the random effects in the SAS GLIMMIX procedure. For the geochemical data,
linear mixed models were fitted in the SAS MIXED procedure. The data were checked for
normality and homoscedasticity on model residuals using quantile-quantile (Q-Q) plots,
histograms and studentized residual plots. Different transformations were tried in an attempt to
satisfy the assumption underlying the ANOVA model (Piepho, 2009). A full factorial model
was specified with the effects of organic inputs (the 3 contrasting organic inputs (ZM, TD, CC)
and the no input control (CON)), mineral N fertilizer (0 and 120 kg N ha-1 rates) and their
interactions as fixed effects on the response variables (i.e., abundance of the 4 target genes and
geochemical properties). Statistical significance of all effects was assessed at a significance
level of 0.05. Blocks in the described factorial model were considered as both fixed and random
effects and model selected accordingly using the Akaike Information Criterion (AIC). PDIFF
option (i.e., table of P values for all possible pairwise comparisons of least square means
(LSMEANS)), as well as the letters (letter display) from the SAS generalized linear mixed
models procedure were used to separate treatment means/medians. Twenty-four observations
(8 treatments, replicated 3 times as described in section 2.2) and presented in descriptive
statistics (Fig. 2.1; Table 2.2) were used for all statistical analyses. It is however, worth
mentioning that for abundance of all 4 genes no significant interactions between the 2 factors
“Organic input” and “Mineral N fertilizer” were determined (Table 2.3). This was also the case
for geochemical data except HWEC (Table 2.5). Therefore, treatment codes in figure 2.2 and
table 2.5 (i.e., CON, CC, ZM, TD) were irrespective of the mineral N fertilizer levels (i.e., 0
N, 120 N) and those of mineral N levels irrespective of organic input type. Graphs were created
in Systat software program using SigmaPlot package version 12.5 (San Jose, CA, USA). Linear
regression analyses were conducted in the SAS REG procedure to relate the abundance
(dependent variables) of target genes to the geochemical properties (independent variables).
Soil treatment main and interaction effects on TRFLP data sets generated for each gene were
tested using permutation multivariate analysis of variance (PERMANOVA) (Anderson, 2001).
Treatment effects were further assayed based on Bray-Curtis similarity coefficients (Rasche et
al., 2011; Rees et al., 2005). A similarity matrix was generated for all possible pairs of samples
33
for each target gene. The similarity matrix was used for analysis of similarity (ANOSIM) to
test if composition of soil microbial communities was altered by different quality organic inputs
and combinations with mineral N fertilizer. ANOSIM is based on rank similarities between the
sample matrix and produces a test statistic called ‘R’ (Rees et al., 2005). A ‘global’ R was first
calculated in ANOSIM, which evaluated the overall effect of each factor in the data set. This
step was followed by a pairwise comparison, whereby the magnitude of R indicated the degree
of separation between two tested communities. An R score of 1 indicated a complete separation,
while 0 indicated no separation (Rees et al., 2005).
Figure 2.1: Gene copy numbers of the four assayed genes in the different treatments
(descriptive means ± standard deviation, n=3): Total number of observations = 24. (A) total
bacteria (bacterial 16S rRNA gene), (B) total archaea (archaeal 16S rRNA gene), (C) ammonia-
oxidizing bacteria (AOB, bacterial amoA gene), and (D) ammonia-oxidizing archaea (AOA,
archaeal amoA gene) after 10 years of application of different biochemical quality organic
inputs and combination with mineral N. Effect of quality of organic input treatments (grey
bars); Effect of mineral N treatments (white bars): 0 N = 0 kg N ha-1, 120 N = 120 kg N ha-1:
CON = control, CC = Calliandra calothyrsus, TD = Tithonia diversifolia, ZM = Zea mays.
34
Table 2.2: Geochemical properties of the Embu soil treated with different quality organic inputs and combinations with (+) and without (-) N
fertilizer. Values are descriptive means (n =3 ± standard deviation) for soils sampled in respective treatments. Total number of observations = 24.
Values followed by different superscript letters (a-f) in the same column are significantly different (P < 0.05).
Soil property definition: HWEC/N = Hot water extractable carbon/nitrogen, TC = Total carbon, Nt = Total nitrogen, NH4+ = Ammonium, NO3
- =
Nitrate. Treatment codes: CON - N = Control + 0 kg N ha-1, CON + N = Control + 120 kg N ha-1, CC - N = Calliandra calothyrsus + 0 kg N ha-1,
CC + N = Calliandra calothyrsus + 120 kg N ha-1, ZM - N = Zea mays + 0 kg N ha-1, ZM + N = Zea mays + 120 kg N ha-1, TD - N = Tithonia
diversifolia + 0 kg N ha-1, TD + N = Tithonia diversifolia + 120 kg N ha-1.
Geochemical properties
Treatments Soil pH HWEC [mg kg-1] HWEN [mg kg-1] TC [%] Nt [%] NH4+[mg kg-1] NO3
-[mg kg-1]
CON-N 5.09 ± 0.19cd 402.98 ± 29.4c 30.02 ± 2.46c 2.28 ± 0.1b 0.20 ± 0.01c 11.7 ± 2.1 c 3.8 ± 0.5b
CON+N 4.64 ± 0.16f 511.03 ± 72.1abc 61.45 ± 14.39a 2.28 ± 0.09b 0.21 ± 0.01c 48.3 ± 10.7a 25.1 ± 13.8a
CC-N 5.17 ± 0.20bc 498.18 ± 63.8bc 38.07 ± 5.10bc 2.66 ± 0.23a 0.23 ± 0.02b 11.7 ± 1.6c 7.6 ± 0.8b
CC+N 4.73 ± 0.22ef 487.72 ± 48.98bc 62.82 ± 6.41a 2.67 ± 0.23a 0.24 ± 0.03ab 34.1 ± 3.7b 26.9 ± 10.2a
ZM-N 5.44 ± 0.31ab 593.42 ± 160.6ab 42.63 ± 9.43b 2.92 ± 0.37a 0.26 ± 0.03a 11.5 ± 2.0c 7.0 ± 1.4b
ZM+N 4.89 ± 0.13def 544.95 ± 108.6ab 64.34 ± 11.65a 2.70 ± 0.31a 0.24 ± 0.04ab 35.8 ± 10.2ab 15.1 ± 8.1ab
TD-N 5.48 ± 0.20a 622.60 ± 72.8a 46.05 ± 1.65b 2.86 ± 0.15a 0.25 ± 0.02ab 11.9 ± 0.4c 8.1 ± 0.5b
TD+N 4.97 ± 0.05cde 620.82 ± 65.8a 64.88 ± 16.71a 2.80 ± 0.20a 0.25 ± 0.03ab 33.9 ± 14.2b 24.0 ± 13.0a
35
Table 2.3: Statistical evaluation of gene abundance and community composition by full
factorial analysis of variance (ANOVA) and permutation multivariate analysis of variance
(PERMANOVA) respectively
Effects/Source of variation
Microbial groups Organic input Mineral N Input x Mineral N
qPCR
Total bacteria ** ** n.s
Total archaea ** * n.s
Ammonia-oxidizing bacteria
(AOB)
** n.s n.s
Ammonia-oxidizing archaea
(AOA)
** ** n.s
TRFLP
Total bacteria n.s * n.s
Total archaea n.s ** n.s
Ammonia-oxidizing bacteria
(AOB)
** * n.s
Ammonia oxidizing archaea
(AOA)
n.s n.s n.s
Significance levels: n.s: P > 0.05; *P < 0.05; **P < 0.01.
For graphical visualization of the relationship between compositions of prokaryotic
communities with soil chemical data, Canonical Analysis of Principal coordinates (CAP) was
performed on resemblance matrix data generated based on Bray-Curtis similarity coefficients
(Anderson and Ter Braak, 2003; Anderson and Robinson, 2003; Clarke, 1993). In addition,
regression analyses were performed using Distance-based linear models (DSTLM) (Boj et al.,
2011; Legendre and Anderson, 1999; McArdle and Anderson, 2001. These analyses were used
to test the relationship between the composition of AOB, AOA and total communities based
on Shannon-Weaver indices (H´, Shannon and Weaver, 1949) (predicted variables) and soil
chemical properties (predictor variables). DISTLM is a routine for analyzing the relationship
between a multivariate data, based on resemblance matrix, and one or more predictor variables
(Legendre and Anderson, 1999; McArdle and Anderson, 2001). It partitions variation in a data
set (i.e., community composition of target genes, soil chemical data) according to regression
models. PERMANOVA, ANOSIM, CAP and DISTLM analyses were conducted using
Primer6 for Windows (version 6.1.13, Primer-E Ltd., Plymouth, UK) with (PERMANOVA+
version 1.0.6) add-on for Primer 6 software (Anderson, 2001).
36
2.3 Results
2.3.1 Microbial abundance
Copy numbers of bacterial 16S rRNA genes ranged from 4.21 x 109 to 8.10 x 109 per gram dry
soil in CON and TD plots, respectively (Fig. 2.2A). With regard to organic input quality, TD
yielded significantly higher total 16S rRNA gene copy numbers compared to CC (P < 0.05).
Use of mineral N reduced bacterial 16S rRNA gene copy numbers compared to the zero rates
(P < 0.05).
Copy numbers of archaeal 16S rRNA genes ranged from 5.37 x 107 to 1.79 x 108 per gram dry
soil in CON and TD plots, respectively (Fig. 2.2B). Considering the effects of organic input
quality, CC and ZM yielded significantly lower gene copy numbers compared to TD (P < 0.05).
Similar to bacterial 16S rRNA genes, mineral N irrespective of organic input quality reduced
abundance of archaeal 16S rRNA genes compared to the zero rates (P < 0.05).
Copy numbers of AOB gene ranged from 1.71 x 105 to 1.35 x 106 per gram of dry soil in CON
and TD, respectively (Fig. 2.2C). Regarding the effect of quality of organic inputs, AOB gene
copy numbers under TD were significantly higher than those under CC and ZM (P < 0.01).
Application of mineral N irrespective of organic input did not influence AOB compared to the
zero rates (P > 0.05).
Copy numbers of AOA gene ranged from 2.40 x 107 to 5.20 x 107 per gram of dry soil in CON
and ZM treatments, respectively (Fig. 2.2D). Focusing on the effect of organic input quality,
ZM and TD yielded significantly higher AOA gene copies compared to CC (P < 0.05). Similar
to abundance of total communities, application of mineral N yielded lower AOA gene copies
compared to the zero rates (P < 0.05).
2.3.2 Microbial community composition
Canonical analysis of principal coordinates (CAP) (Fig. 2.3 and 2.4) plots revealed distinct
patterns for all the studied prokaryotic communities apart from the AOA between treatments
which had received mineral N fertilizer from those which did not. Interestingly, the fertilizer
treated soils were located in the direction of increasing NH4+, NO3
- and HWEN particularly for
total communities. On the other hand, distinct patterns with regard to quality of organic inputs
were revealed for AOB and AOA but not for total communities.
37
Figure 2.2: Gene copy numbers of the four assayed genes in the different treatments (means n
= 6): Total number of observations = 24. (A) total bacteria (bacterial 16S rRNA gene), (B) total
archaea (archaeal 16S rRNA gene), (C) ammonia-oxidizing bacteria (AOB, bacterial amoA
gene), and (D) ammonia-oxidizing archaea (AOA, archaeal amoA gene) after 10 years of
application of different biochemical quality organic inputs and combination with mineral N.
Different letters (a-c) above the bars indicate treatments with significant differences (P < 0.05).
Effect of quality of organic input treatments irrespective of mineral N fertilizer levels (grey
bars): CON = control, CC = Calliandra calothyrsus, TD = Tithonia diversifolia, ZM = Zea
mays. Effect of mineral N treatments irrespective of organic inputs (white bars): 0 N = 0 kg N
ha-1, 120 N = 120 kg N ha-1.
38
PERMANOVA results did not reveal significant interactions between organic inputs and
mineral N fertilizer on composition of prokaryotic communities (Table 2.3) (P > 0.05).
Total archaeal, total bacterial and AOB communities were significantly influenced by mineral
N fertilizer, whereas quality of organic inputs revealed significant effects on AOB (P < 0.01).
Analyses of similarity (ANOSIM) of TRFLP fingerprints revealed shifts of community
composition of bacterial amoA (AOB) (Global R = 0.257) and archaeal amoA (AOA) (Global
R = 0.189) genes due to quality of organic inputs (P < 0.05), while no significant effect on
composition of total communities was revealed (Table 2.4). For pairwise comparisons between
organic input treatments irrespective of mineral N fertilizer, the highest community
composition separation of AOB was shown between CON and TD (R = 0.535). TD versus ZM
showed for AOB an R = 0.343, while an R = 0.320 was calculated between CON and ZM. CON
versus CC presented an R = 0.231, but no separation of AOB was found between TD versus
CC and CC versus ZM. For AOA, pairwise comparisons showed an R = 0.346 between CON
and ZM, while an R = 0.285 and R = 0.209 was calculated between TD and ZM as well as TD
and CC, respectively. Meanwhile, irrespective of organic inputs, mineral N fertilizer addition
altered the composition of total bacterial (Global R = 0.202) and total archaeal (Global R =
0.404) communities (P < 0.05), but AOB and AOA were not affected (data not shown).
ANOSIM results were to a larger extent in agreement with CAP ordination plots and
PERMANOVA results.
2.3.3 Geochemical properties
No interaction between combination of organic inputs and mineral N on geochemical
properties was determined except for HWEC (P > 0.05). Therefore, the main effects of organic
inputs and mineral N results only were displayed (Table 2.5). ZM (pH = 5.18) and TD (5.23)
showed higher soil pH values compared to CON (4.87) and CC (4.96) (P < 0.05). Lower pH
values were recorded in all plots which received mineral N fertilizer compared to those that did
not (P < 0.001). All pH values decreased in comparison to the start of experiment in 2002
(5.81) (Gentile et al., 2011a). TD increased HWEC values compared to CON and CC (P <
0.05). On the other hand, mineral N did not influence HWEC contents (P > 0.05). HWEN was
not influenced by organic input quality (P > 0.05). However, mineral N increased HWEN
values (P < 0.001).
TD treated plots had higher TC values compared to CON and CC (P < 0.05). Nt values were
increased by application, but not quality of organic inputs compared to CON (P < 0.05).
Application of mineral N did not influence TC and Nt contents (P > 0.05). TC and Nt remained
39
relatively constant in all treatments, but were reduced in CON compared to the start of
experiment in 2002 (Gentile et al., 2011a). CON showed highest NH4+ over all the other
treatments (P < 0.05). NO3- was not influenced by quality or application of organic inputs (P
> 0.05). Mineral N received treatments increased NH4+ and NO3
- compared to sole use of
organic inputs or the no input control (P < 0.001).
Figure 2.3: Canonical Analysis of Principal coordinates (CAP) for visual presentation of
patterns of composition of prokaryotic communities as shaped by mineral N and their
relationship with soil chemical properties. Prokaryotic communities: A = Total bacteria, B
=Total archaea, C = Ammonia-oxidizing bacteria and D = Ammonia-oxidizing archaea.
Treatments codes: 0 N = 0 kg N ha-1, 120 N = 120 kg N ha-1.
(A) (B)
(C) (D)
40
2.3.4 Regression analysis
Soil pH, HWEC, TC and Nt were the strongest predictors of shifts in abundance of archaeal
and bacterial 16S rRNA as well as archaeal amoA genes (Table 2.6). An exception was AOB
abundance, where HWEC, TC and Nt had generally a weak influence. Specifically, up to 80
%, 75 % and 60 % increase in abundance of total bacteria, AOA and total archaea respectively
was explained by soil pH. Up to 60 %, 57 % and 52 % increase in abundance of archaeal
communities (i.e., total archaea and AOA), total bacteria and AOB respectively was accounted
for by HWEC. Up to 70 % increase in abundance of AOA, total bacteria, total archaea and 45
% for AOB was explained by TC. Up to 60 % increase in abundance of AOA, total archaea,
total bacteria and 40 % for AOB was accounted for by Nt. Up to 50 % and 47 % decrease in
abundance of AOA and total bacterial respectively was attributed to NH4+, while up to 36 %
decrease in total bacterial abundance was attributed to NO3-. On the microbial composition
results (Table 2.6) up to 60 %, 50 % and 33 % change in total bacterial, total archaeal and AOA
communities’ composition respectively was explained by soil pH. Up to 17 % and 25 % change
in composition of AOB was explained by HWEC and TC respectively. Approximately 28 %,
20 % and 17 % shift in total bacteria, AOA and AOB compositions respectively was attributed
to Nt. Up to 48 % and 33 % change in total archaea was attributed to NH4+ and NO3
-
respectively.
41
Figure 2.4: Canonical analysis of principal coordinates (CAP) for visual presentation of
patterns of composition of prokaryotic communities as shaped by contrasting biochemical
organic inputs and their relationship with soil chemical properties. Prokaryotic communities:
A = Total bacteria, B =Total archaea, C = Ammonia-oxidizing bacteria and D = Ammonia-
oxidizing archaea. Treatments codes: CON = Control, TD = Tithonia diversifolia, CC =
Calliandra calothyrsus, ZM = Zea mays.
(A)
(D) (C)
(B)
42
Table 2.4: Analysis of similarity (ANOSIM) to determine the influence of biochemical quality of organic inputs on
the community structure of total and ammonia-oxidizing prokaryotes in the SOM field experiment.
Bacterial 16S rRNA Archaeal 16S rRNA AOA AOB
Global R 0.089 -0.053 0.189 0.257
Significance level across
groups
n.s
n.s
*
*
Total bacteria Total archaea AOA AOB
Pairwise comparisons R statistics
CON vs. TD 0.237 -0.048 0.109 0.535
CON vs. CC 0.270 -0.067 0.178 0.231
CON vs. ZM 0.041 -0.069 0.346 0.320
TD vs. CC 0.065 -0.013 0.209 0.081
TD vs. ZM -0.05 -0.081 0.285 0.343
CC vs. ZM -0.011 -0.041 -0.074 0.052
Global R indicates the degree of separation of communities within each microbial group.
Significance levels: n.s: P > 0.05; *P < 0.05.
Treatment codes: CON = Control, CC =Calliandra calothyrsus, ZM = Zea mays, TD = Tithonia diversifolia.
R statistics indicates the degree of separation of communities between two treatments, with a score of 1
indicating complete separation and 0 indicating no separation.
43
Table 2.5: Geochemical properties of the Embu soil showing main effects of different quality organic inputs and mineral N treatments. Values
((means or medians) n = 24) followed by different superscript letters (a-b/A-B) in the same column are significantly different (P < 0.05). SED
represents standard error of the difference between two means for comparisons across inputs and N fertilizer treatments.
Geochemical properties
Treatments Soil pH HWEC [mg kg-1] HWEN [mg kg-1] TC [%] Nt [%] NH4+[mg kg-1] NO3
- [mg kg-1]
CON 4.9b 485b 46a 2.24b 0.21b 23a 9a
CC 5.0b 504b 51a 2.63b 0.24a 20ab 16a
ZM 5.2a 606ab 54a 2.75ab 0.25a 20ab 10a
TD 5.2a 630a 56a 2.81a 0.26a 17b 15a
SED ± 0.1 ± 31 ± 5 ± 0.01
0 N 5.3A 549A 40B 2.64A 0.24A 12B 6B
120 N 4.8B 563A 64A 2.55A 0.23A 35A 20A
SED ± 0.1 ± 15 ± 4 ± 0.004
Source of variation and statistical significance
OI ** *** n.s *** *** n.s ns
N *** n.s *** * n.s *** ***
OI x N n.s *** n.s n.s n.s n.s ns
Parameters without SED values show the treatments (medians) after back transformation of the least square means (lsmeans) from analyses.
OI = organic inputs; N = Mineral fertilizer.
Soil property definition: HWEC/N = Hot water extractable carbon/nitrogen, TC = Total carbon, Nt = Total nitrogen, NH4+ = Ammonium, NO3
- =
Nitrate. Treatment codes: CON = control, CC = Calliandra calothyrsus, ZM = Zea mays, TD = Tithonia diversifolia, 0 N = 0 kg N ha-1, 120 N =
120 kg N ha-1. Significance levels: n.s: P > 0.05; *: P < 0.05; **: P < 0.01; ***: P < 0.001.
44
Table 2.6: Regression analysis (R2) showing relationships between abundance and community composition of prokaryotic
genes and geochemical data.
Geochemical
property
Prokaryotic group
Total bacteria Total archaea Ammonia-oxidizing
bacteria (AOB)
Ammonia-oxidizing
archaea (AOA)
qPCR TRFLP qPCR TRFLP qPCR TRFLP qPCR TRFLP
Soil pH 0.802*** n.s 0.597*** 0.512*** n.s n.s 0.747*** 0.333**
HWEC 0.573*** n.s 0.619*** n.s 0.517** 0.175* 0.599*** n.s
HWEN n.s n.s n.s n.s n.s n.s n.s n.s
TC 0.713*** n.s 0.694*** n.s 0.456* 0.252* 0.743*** n.s
Nt 0.607*** 0.202* 0.635*** n.s 0.412* 0.283* 0.645*** 0.173*
NH4+ 0.472** n.s n.s 0.479*** n.s n.s 0.515** n.s
NO3- 0.360* n.s n.s 0.333** n.s n.s n.s n.s
Geochemical property definition: HWEC/N = Hot water extractable carbon/nitrogen, TC = Total carbon, Nt = Total nitrogen,
NH4+ = Ammonium, NO3
- = Nitrate.
Significance levels: n.s: P > 0.05; *: P < 0.05; **: P < 0.01; ***: P < 0.001.
45
2.4 Discussion
2.4.1 Organic input quality effects on microbial communities
Low quality (Z. mays, ZM) and intermediate quality (C. calothyrsus, CC) as opposed to high
quality (T. diversifolia, TD) organic inputs decreased the abundance of bacterial amoA genes
(AOB). In support of this finding, CC revealed lower TC and HWEC values than TD.
Accordingly, over 45 % increase in AOB abundance was attributed to TC and HWEC
independently. This corroborated the vital role of organic C in regulation of AOB abundance.
This finding was in line with the significant organic input quality influence on the community
composition of AOB. In this respect, TD promoted AOB abundance compared to ZM which
matched a significant community composition separation. This difference was explained by
contrasting biochemical qualities of organic inputs, where both input types (TD, ZM) had
similar lignin and polyphenol proportions, but differed greatly in C/N ratio (Gentile et al.,
2011a, 2011b) availing high and easily accessible NH3- to AOB compared to ZM. Support for
this was further given by AOB community composition similarities between TD and CC which
both had contrasting lignin and polyphenol proportions, but similar C/N ratios. This fact also
revealed that presence of polyphenols, leading to input type specific formation of polyphenol-
protein complexes (Chivenge et al., 2011; Gentile et al., 2011a; Mutabaruka et al., 2007), did
not pose a regulating effect on AOB community composition; although they reduced its
abundance due to reduced C and N accessibility. Similarly, Wessén et al. (2010) reported a
distinct AOB community composition between straw with a lower C/N ratio (high ammonia
availability) compared to peat treatments in the Ultuna long-term experiment in Sweden.
Contrastingly, abundance of archaeal amoA genes (AOA) was promoted by both ZM and TD
inputs in comparison to CC inputs. This finding was linked to increased soil pH in TD and ZM
treatments as opposed to CC, emphasizing the sensitivity of AOA to pH changes in comparison
to AOB as further discussed in the next section of this paper. AOA abundance was supported
by a weak but significant community composition separation between TD versus CC and ZM.
These findings revealed that the difference in C/N ratios between low and high quality organic
inputs (Gentile et al., 2011a, 2011b), which controls the availability of organic N, did not pose
a regulating effect on AOA abundance, although it altered its community composition. Similar
effects of different C/N ratios on AOA community composition were reported by Wessén et
al. (2010).
46
AOA are capable of exploring alternative sources of energy and nutrients especially in N
limited soils (Levičnik-Höfferle et al., 2012) as was observed under ZM treatment. The
variations in abundance and composition of AOB and AOA in response to organic input quality
suggested their niche differentiation in soils with organic N limitation. These findings
contrasted others, where formation of polyphenol-protein complexes induced positive shifts of
other microbial groups such as proteolytic bacterial and fungal decomposer communities in
sandy soils (Kamolmanit et al., 2013; Mutabaruka et al., 2007; Rasche et al., 2014). This
uncertainty raises the need for prospective work to verify if effects of polyphenol-protein
complexes are specific to particular microbial groups and soil types. Overall, AOA abundance
dominated that of AOB in our study. This finding was supported by strong relationships
between AOA abundance and soil chemical properties (i.e., HWEC, TC and Nt) compared to
that of AOB. It could therefore be deduced that AOA are more adapted to acquisition of organic
nutrients compared to AOB particularly when the soils are moisture limiting, considering that
we conducted soil sampling during a dry spell. This scenario was linked to substrates limitation
associated with water limitation (Stark and Firestone, 1995) meaning that AOA are water stress
tolerant compared to AOB. In support of this, Gleeson et al. (2010) demonstrated sensitivity of
AOB abundance to water availability whereas AOA remained unaffected. Therefore, the
response of AOB and AOA to organic input quality may also have been shaped by
environmental factors like soil moisture contributing to their niche differentiation in soils
(Erguder et al., 2009; Gleeson et al., 2010; Wessén et al., 2010).
Similar to the ammonia-oxidizing communities, the abundance of total bacterial 16S rRNA
genes was significantly lower in CC compared to TD, whereas total archaeal abundance was
significantly lower in both CC and ZM in comparison to TD. Contrastingly, community
composition of the same genes remained unchanged. These findings were supported by
geochemical properties (i.e., HWEC, TC) showing significant responses to biochemical quality
of studied organic inputs, and were further supported by positive correlations between
geochemical properties and gene abundance. The observed organic input effects were mainly
explained by high polyphenols and lignin contents in CC as opposed to ZM and TD. Resulting
polyphenol-protein complexes in CC shield organic N (i.e., proteins), but also C from microbial
access (Hagerman, 2012; Schmidt et al., 2013; Verkaik et al., 2006). A previous incubation
study with soil from the same field experiment revealed that organic input quality efficiently
controlled short-term soil C dynamics, although the anticipated mid-term (three year field trial)
effect on the soil organic matter pool was not detectable (Chivenge et al. 2011; Gentile et al.,
2011a, 2011b). On the other hand, Nziguheba et al. (2005) did also not observe any effect of
47
input quality on soil C and N dynamics after five seasons of incorporation of similar organic
inputs differing in their biochemical quality. Moreover, Rasche et al. (2014) reported an
absence of organic input quality effects on bacterial 16S rRNA gene abundance and community
composition in the same field experiment after 9 years. Consequently, we argue that organic
input quality regulated the abundance of particular groups of the soil microbial community, but
was at the current state of the field experiment not the primary driving actor in altering the
community composition, although short-term changes may occur directly after input
application (Chivenge et al., 2011; Gentile et al., 2011a).
We suggest that the observed stability of the total microbial community composition
irrespective of organic input quality was most probably related to the clayey structure of the
soil under the long-term field experiment providing a strong background signal of soil organic
carbon (SOC) (Bossuyt et al., 2001; Fonte et al., 2009; Gentile et al., 2011a, 2011b). This
suggested that the soil type per se but not organic input quality might have regulated to a large
extent the composition of the total microbial community (Rasche et al., 2014). This assumption
was supported by Neumann et al. (2013) and Sessitsch et al. (2001) who confirmed that particle
size fractions were more responsible for distinct alterations of bacterial community
composition than the use of organic amendments. The dominance of clay in our soil could have
thus masked the hypothesized alterations of total microbial community composition in
response to different organic inputs. However, this speculative assumption clearly raises the
need for prospective research if distinct soil types with their individual soil textures and
aggregation processes and hence SOC stabilization are more responsible for the determination
of the composition of microbial communities including ammonia-oxidizers over other factors
such as the use of contrasting organic soil amendments (Rasche et al., 2014). Additionally, it
will be necessary to enhance the currently limited understanding on the potential of
biochemically contrasting organic inputs and accompanied microbial decomposition processes
to shape the physical properties (e.g., aggregation) of soils (Kunlanit et al., 2014; Puttaso et al.,
2013).
2.4.2 Mineral N fertilizer depresses microbial abundance except AOB
Use of mineral N in combination with organic inputs was marked by a significant reduction in
soil pH (4.8) compared to treatments which did not receive mineral N (pH = 5.3). This was
associated with a substantial depression of the abundance of AOA as well as total bacteria and
archaea. The latter two revealed also a significant alteration in their community composition.
Similar trends were observed in the Ultuna long-term field experiment (Hallin et al., 2009).
48
Our findings were attributed to the indirect effects of long-term application of NH4+ fertilizers
through soil pH reduction (Geisseler and Scow, 2014). The acidification effect due to NH4+
fertilizer might have per se inhibited microbial growth, but may have also promoted nitrifying
activities through provision of NH4+ substrate explaining high NO3
- in the plots receiving
mineral N fertilizer. This direct effect was also supported by lack of significant differences in
NO3- concentrations between the studied organic treatments. Though AOA dominated this
strongly acidic soil compared to AOB, a finding in line with Prosser and Nicol (2012), stability
of AOB demonstrated its obviously higher resilience to reduction in soil pH compared to AOA
(Wessén et al., 2010).
Overall, use of mineral N fertilizer in this study altered AOB but not AOA community
composition according to CAP and PERMANOVA analyses. In line with our finding, Wu et
al. (2011) found distinct shifts in community composition of AOB, but not for AOA in
fertilized soils (180 kg urea N ha-1 yr-1 combined with rice straw over no input controls) in a
22 year old field experiment in China. Similarly, it was recently shown that AOA community
composition was not stimulated by inorganic NH3- amendments (Stopnišek et al., 2010;
Verhamme et al., 2011). In our study, the variation in abundance and community composition
of AOB and AOA in response to mineral fertilizer suggested their niche specialization despite
their functional redundancy. In another laboratory microcosm experiment, AOB community
composition responded rapidly to NH4+ amendments within a few weeks but their AOA
counterpart remained unaffected (Avrahami et al., 2003). Similarly, Jia and Conrad (2009) and
Wang et al. (2009) demonstrated changes in AOB but not AOA composition with use of NH4+
fertilizer. This suggested that AOB mainly utilized NH3- from fertilizer application, while AOA
most probably utilized NH3- as N source from mineralization of organic inputs and soil organic
matter. These assumptions were justified by negative correlations (data not shown) between
NH4+ and AOA indicating that application of NH4
+ did not promote AOA (Schleper, 2010;
Valentine, 2007). These results corroborated that AOB had a competitive advantage to
immobilize N supplied with N fertilizer, hence promoting their abundance although the
promotion did not supersede that of sole use of organic inputs. Additionally, mineral fertilizer
application was shown to reduce soil aggregate stability through accelerated aggregate turnover
potentially reducing SOC stabilization (Bossuyt et al., 2001; Fonte et al., 2009). Moreover,
oxygen limitation due to disruption of microbial protection sites associated with reduced
aggregate stability could also have led to the reduction of microbial abundance. The two
speculations need however further corroborating research.
49
2.5 Conclusions and recommendations
Contrasting biochemical quality of organic inputs (CC, ZM, TD) regulated the abundance of
ammonia-oxidizers and total communities. Overall, the high quality organic input (TD)
increased while the intermediate quality organic input (CC) reduced the abundance of assayed
genes. However, influence of low quality organic input (ZM) on abundance was specific to the
different genes. On the other hand, effects of organic input types on community composition
were only of minor extent compared to those on abundance. It was thus concluded that the
duration of the SOM field experiment of ten years might not have been sufficient to induce
distinctions in the composition of assayed total prokaryotic communities. This assumption was
supported by Kamaa et al. (2011) and Shen et al. (2010). Their findings on long-term field
experiments revealed similar compositions of total bacterial communities in farm yard and
composted pig manure treated soils compared to unfertilized controls. Hence, a prolonged
duration of the SOM field experiment is strongly required to determine the actual time point
when organic input quality effects on composition of the total soil microbial decomposer
communities as studied by 16S rRNA genes can be verified.
This study focused on the assessment of the functional potential of soil ammonia-oxidizing
communities as was based on DNA-based quantification of amoA gene abundance. To further
understand the dynamics of ammonia-oxidizers as regulated by the biochemically contrasting
organic inputs, we recommend rRNA-based studies along with nucleic acid stable isotope
probing (España et al., 2011) to demarcate those community members which are actually
activated by application of individual organic inputs. This strategy should also include
phylogenetic characterizations of microbial communities (Junier et al., 2010).
It is worth noting that this study was established on one occasion soil sampling to elaborate the
long-term effects of tested treatments. We recommend prospective research with repeated
measurements during critical growth stages of the test crop (Hai et al., 2009) to consider
seasonal dynamics of microbial decomposer communities to separate temporal responses to
freshly applied organic inputs (direct effects) versus those to altered soil organic matter quality
induced by different organic and inorganic inputs in the long-term (secondary effects)
(Kunlanit et al., 2014). This will provide the required baseline to better understand the
presumed, but not detected interacting effects of organic and inorganic inputs on soil
decomposer microbial communities. Such studies need to include, apart from physico-chemical
soil properties, also other factors such as agro-ecologies as well as rainfall and temperature
patterns which may influence short-term alterations of targeted soil microbial decomposer
communities.
50
51
Chapter 3 Dynamics of bacterial and archaeal amoA gene abundance after additions of
organic inputs combined with mineral nitrogen to an agricultural soil2
Esther K. Muema, Georg Cadisch, Mary K. Musyoki, Frank Rasche
Institute of Plant Production and Agroecology in the Tropics and Subtropics, University of
Hohenheim, Stuttgart, Germany
2 This chapter has been accepted as: Esther K Muema, Georg Cadisch, Mary K Musyoki, Frank Rasche. Dynamics of bacterial and
archaeal amoA gene abundance after additions of organic inputs combined with mineral
nitrogen to an agricultural soil. Journal of Nutrient Cycling in Agroecosystems.
52
Abstract
Dynamics of ammonia-oxidizing bacterial (AOB) and archaeal (AOA) abundance was assayed
in a tropical Humic Nitisol during two cropping seasons of a long-term field experiment
situated in the central highlands of Kenya. Since 2002, soils were treated yearly with
biochemically contrasting organic inputs (4 Mg C ha-1 year-1) of Tithonia diversifolia (TD; C/N
ratio: 13, lignin: 8.9 %; polyphenols: 1.7 %), Calliandra calothyrsus (CC; 13; 13; 9.4) and Zea
mays (ZM; 59; 5.4; 1.2) combined with and without 120 kg CaNH4NO3 ha-1 season-1. In 2012
and 2013, soils (0-15 cm) were sampled at young growth (EC30) and flowering (EC60) stages
of maize and subjected to DNA-based amoA gene quantification. ZM and TD increased AOB
abundance by 9 % and 19 %, respectively, compared to CC, while AOA remained unaffected.
This was ascribed to high organic N in TD and lower lignin and polyphenol contents in ZM
than in CC. In CC, formation of polyphenol-protein complexes limited microbial access to N.
Sole use of mineral N or its combination with organic inputs decreased AOA abundance by 35
% but not AOB as a consequence of pH reduction by mineral N. Overall, AOB was more
responsive than AOA to input quality that became most pronounced under optimal soil
moisture conditions found in 2013 and at EC60 in 2012. We recommend prolonged study
periods, considering also rRNA-based analyses to explore the dynamics of active nitrifying
communities as a consequence of interrelations of contrasting organic inputs, crop growth
stages and seasonality in agricultural soils.
Keywords: Ammonia-oxidizing bacteria and archaea; Gene abundance; Organic input quality;
Mineral nitrogen fertilizer; Crop growth stages; Seasonality.
3.1 Introduction
Ammonia (NH3) oxidation is the first and the most rate-limiting step of nitrification in the
terrestrial nitrogen (N) cycle (Martens-Habbena et al. 2009). It is catalyzed by bacteria (AOB)
and archaea (AOA) through synthesis of the amoA gene encoding the α-subunit of the enzyme
ammonia monooxygenase (Prosser and Nicol 2008; Zhang et al. 2013). In agricultural soils,
NH3 as critical energy source for nitrifying prokaryotes is mainly supplied by mineral N
fertilizers and organic inputs (e.g., crop residues, green manures). The amount and particularly
the biochemical quality (e.g., content of N, cellulose, lignin, polyphenols) of organic inputs is
a central regulator of decomposition and mineralization rates, hence N release (Palm et al.
2011b; España et al. 2011; Rasche and Cadisch 2013; Kunlanit et al. 2014). Combined effects
53
of organic inputs of contrasting biochemical quality and mineral N fertilizer on temporal
variation of AOB and AOA abundance under field conditions are however not adequately
understood (Hai et al. 2009; Wessén et al. 2010).
In reference to the source and quantity of NH4+ (Di et al. 2010), AOA generally dominate their
AOB counterparts in low ammonium (NH4+) conditions or when NH4
+ was derived through
mineralization of organic N (Schauss et al. 2009; Zhang et al. 2010; Levičnik-Höfferle et al.
2012). On the other hand, AOB have been shown to dominate under high NH4+ conditions,
especially when supplied through mineral fertilizers (Schleper et al. 2005; Jia and Conrad 2009;
Di et al. 2009). Hai et al. (2009) confirmed a promoted abundance of AOB by urea-derived
NH4+, while AOA abundance was primarily stimulated by organic inputs derived from
sorghum straw and cattle manure. These results clearly corroborate the acknowledged niche
partitioning between AOB and AOA in reference to the source and concentration of NH4+ in
soils (Martens-Habbena 2009; Schleper 2010; Prosser and Nicol 2012).
The C/N ratio of organic inputs has been used as a common indicator explaining AOB and
AOA abundance dynamics (Millar and Baggs 2004; Wessén et al. 2010; Strauss et al. 2014).
It is, however, not yet fully understood to which extent other biochemical quality attributes like
polyphenols and lignin as constituents of organic input derived C limit the N availability
through formation of polyphenol-protein bonds (Palm et al. 2001a; Millar and Baggs 2004;
Schmidt et al. 2013) and how this controls the abundance of AOB and AOA. Polyphenol-
induced N limitation induced a critical stress response through an increase in abundance of
proteolytic bacteria in sandy agricultural soils (Rasche et al. 2014). On the other hand,
abundance of AOB and AOA did not respond to exposure to purified polyphenols during short-
term incubation periods (Schmidt et al. 2013). This lack of response of AOB and AOA
abundance was probably the consequence of the absence of complexation of artificially
introduced polyphenols with proteins in soils (Schmidt et al. 2013). This situation is, however,
likely to change with the exposure of AOB and AOA to freshly incorporated protein rich
organic inputs differing in their potential to bind proteins by polyphenols and lignin in soils.
This hypothesis needs further verification, especially when organic inputs are combined with
mineral N fertilizers to compensate organic input induced N limitation (Palm et al. 2001a;
Partey et al. 2013).
To gain in depth understanding of the suggested effects of either organic or mineral inputs on
nitrifying communities in soils, abiotic factors need to be considered. Water stress through
reduced precipitation decreased AOB and AOA abundance in soils under young growth (EC30)
to the flowering (EC60) stage of sorghum (Hai et al. 2009). Rasche et al. (2011) reported
54
increased AOB abundance with increased precipitation, while that of AOA increased with
decreasing temperatures in a temperate beech forest. Notably, Rasche et al. (2011) in contrast
to others (e.g., Leininger et al. 2006; Wessén et al. 2010), focused on the interrelated effects of
seasonality and resource availability on nitrifying soil communities in a two year study, rather
than deriving conclusions only on a single study year. Such extended study periods need to be
also considered for tropical environments, where unreliable climatic conditions (e.g., erratic
precipitation patterns) may interfere with target research questions on e.g. organic and mineral
input dependent soil microbial C and N dynamics. In this respect, repeated measurements at
distinct crop growth stages during individual cropping seasons may allow a sound
interpretation of dynamics of functionally relevant microbial groups (e.g., AOB, AOA) under
the control of specific organic and mineral fertilization schemes.
The primary objective of this 2-year study was to assess the dynamics of AOB and AOA
abundance (DNA-based amoA gene quantification) under the influence of biochemically
contrasting organic inputs with and without mineral N fertilizer in a tropical agricultural soil.
To evaluate the presumed fluctuations of AOB and AOA during the initial decomposition phase
of both study seasons, we conducted soil samplings at EC30 and EC60 stages of maize used as
test crop. Firstly, we hypothesized higher AOB and AOA abundance under high quality organic
inputs at EC30 due to the faster organic N release as opposed to intermediate quality inputs
with high polyphenol and lignin contents and low quality inputs with low N contents. We also
presumed a reduction of AOB and AOA abundance by high quality inputs at EC60 due to
negligible remnants of available N following faster decomposition rates at EC30. A contrasting
effect was presumed for intermediate and low quality inputs as traced back to the gradual
decomposition and mineralization. Secondly, we hypothesized an increase of AOB but not
AOA abundance in soils treated with a combination of intermediate or low quality inputs with
mineral N fertilizer compared to their counterparts without fertilizer N. This was justified by
the presumed compensation effect of mineral N fertilizer under N limiting conditions induced
by intermediate and low quality inputs.
3.2 Materials and methods
3.2.1 Site description and field experiment design
The experimental site was located in Embu (0°30´ S, 37°27´ E; 1380 m above sea level) in
the central highlands of Kenya (130 km northeast of Nairobi). The soil is defined as a Humic
Nitisol (FAO 2006; Jones et al. 2013) dominated by kaolinite minerals derived from basic
55
volcanic rocks. The texture of the topsoil layer (0-15 cm) was characterized by 17 % sand, 18
% silt and 65 % clay and contained 29.4 g kg-1 organic C, 2.7 g kg-1 total N and had a pH
(H2O) of 5.81 at the start of the field experiment in 2002 (Chivenge et al. 2009; Gentile et al.
2011a). The site has an annual mean temperature of 20°C and a mean annual rainfall of 1200
mm. Rainfall is bimodal with long season rains received from mid-March to June and short
season rains from mid-October to December (Fig. 3.1). Comparing the long rain seasons, i.e.,
mid-March to mid-June, during which the soil samplings were conducted, year 2013 was
drier (507 mm rainfall) than 2012 (720 mm).
The soil organic matter (SOM) field experiment was established in year 2002 to primarily study
the effect of continuous annual application of biochemically contrasting organic inputs with
and without inorganic mineral N fertilizer on crop performance as well as soil C and N
dynamics (Chivenge et al. 2009; Gentile et al. 2011a; Chivenge et al. 2011). Five different
organic inputs including Grivellia robusta (sawdust) and goats manure, Tithonia diversifolia,
Calliandra calothyrsus and Zea mays were considered at two application rates (i.e., 1.2 and 4
Mg C ha-1 year-1) including a control treatment. The experiment is laid out in a split plot design
with three replicates per treatment with organic inputs as main plots (size, 12 m x 5 m) and
mineral N fertilizer as sub-plots (size, 6 m x 5 m) placed inside the organic input plots
(Chivenge et al. 2009; Gentile et al. 2009). A corridor of 50 cm in width separated the plots to
avoid interference of treatments. The entire experiment consisted of 66 plots (i.e., the five
organic inputs at two application rates plus a control, all with versus without mineral N (i.e., 0,
120 kg N ha-1 season-1) which were replicated three times) (Chivenge et al. 2009). In the
presented study which focused only on twenty four plots, we considered the treatments with
high quality Tithonia diversifolia (C/N ratio: 13, Lignin: 8.9 %; Polyphenols: 1.7 %),
intermediate quality Calliandra calothyrsus (13; 13 %; 9.4 %) and low quality Zea mays (59;
5.4 %; 1.2 %) inputs and a control (Gentile et al. 2011b). At the onset of long rains in each
year, organic inputs (leaves, petioles and small branches for C. calothyrsus in addition to stems
for T. diversifolia and Z. mays stover) are collected and analyzed for dry matter as well as total
C and N content. This data is used to determine the amount of each organic material to be
applied in fresh weight condition at a rate of 4 Mg C ha-1 year-1. These included; (80, 81 and
102) kg of fresh weight of TD, CC and ZM respectively sub-plot-1 year-1. Prior to maize sowing,
organic inputs were chopped into small pieces, broadcast and manually incorporated at a soil
depth of 0-15 cm (Gentile et al. 2011a; Chivenge et al. 2011). In the sub-plots of each plot,
mineral N fertilizer was applied as calcium ammonium nitrate (CaNH4NO3) at a rate of 0 and
120 kg N ha-1 growing season-1 (Chivenge et al. 2009; Gentile et al. 2011a; Gentile et al.
56
2011b). One third of the mineral N fertilizer was applied 3 weeks after sowing of maize (test
crop) and the remainder 8 weeks later (Chivenge et al. 2009). Mineral N fertilizer was
recommended as a complement for the low and intermediate quality organic inputs to
compensate the presumed N limitation (Palm et al. 2001a; Partey et al. 2013). For treatment
comparison purposes in the presented study, mineral N fertilizer was also combined with the
high quality organic input. All plots received a blanket basal application of 60 kg P ha-1 season-
1 and 60 kg K ha-1 season-1 before sowing.
Figure 3.1: Rainfall and temperature distribution at Embu site from year 2012 to 2013.
3.2.2 Soil sampling
After incorporation of fresh organic inputs and application of mineral N fertilizer in 2012 and
2013, soil samples were collected twice each season (i.e., EC30: last week of April 2012, mid-
April 2013; EC60: first week of June 2012, last week of May 2013) (Milling et al. 2005) from
the plots designated for maize cropping. The following 8 treatments were considered: 4
treatments without mineral N fertilizer including the no input control (CON) and 3 organic
57
input treatments (i.e., C. calothyrsus (CC), Z. mays (ZM) and T. diversifolia (TD)). The other
4 treatments were their respective counterparts receiving mineral N fertilizer. In each plot, 10
soil sub-samples were randomly obtained at a depth of 0-15 cm using a soil auger. These 10
sub-samples were bulked to one composite, representative sample per plot, sieved (2-mm
mesh) and stored at 4°C. In total, 96 soil samples were obtained. Prior to analysis, each
composite soil sample was split into 3 proportions. A fresh sample was used for immediate
extraction of ammonium (NH4+) and nitrate (NO3
-). The second proportion was air-dried for
analysis of pH, hot water extractable C and N (HWEC/N), total C (TC) and total N (Nt). The
third proportion was freeze-dried and shipped to Germany for microbiological analyses.
3.2.3 Gene abundance analysis
Total soil DNA was isolated from 0.5 g of each freeze dried soil and quantified as described
by Muema et al. (2015). Quantified DNA was stored at -20°C until further analyses. For DNA-
based quantification of target genes (total bacteria and archaea (16S rRNA gene) as well as
ammonia-oxidizing bacteria (bacterial amoA gene; AOB) and archaea (archaeal amoA gene;
AOA), plasmid standards were prepared (Table 3.1), purified (Invisorb Fragment CleanUp kit,
Stratec Molecular GmbH, Berlin, Germany) and quantified (Rasche et al. (2011). The qPCRs
(3 analytical replicates per DNA sample) were optimized, analyzed and quality checked
according to Muema et al. (2015). Gene copy numbers and reaction efficiencies (total bacteria
103 % ± 1 %, total archaea 80 % ± 11 %, AOB 97 % ± 8 %, AOA 77 % ± 10 %) were calculated
using StepOne Software version 2.2.2 (Applied Biosystems) and presented per gram of dry
soil.
58
Table 3.1: Description of primer sets, PCR ingredients and amplification details used for
quantitative PCR analyses.
Target gene Primer (reference) qPCR
All bacteria Eub338f (Lane, D, 1991) 95 °C 5 min; 40 cycles: 95 °C 30s,
55 °C 35s, 72 °C 45s Eub518r (Muyzer et al., 1993)
All archaea Ar109f (Lueders and Friedrich,
2000)
95 °C 5 min; 40 cycles: 95 °C 30s,
52 °C 35s, 72 °C 45s, 78 °C 20s
Ar912r (Lueders and Friedrich,
2000)
Ammonia-
oxidizing bacteria
(AOB)
AmoA-1f (Rotthauwe et al., 1997) 95 °C 5 min; 45 cycles: 95 °C 30s,
57 °C 45s, 72 °C 45s, 78 °C 20s AmoA-2r (Rotthauwe et al., 1997)
Ammonia-
oxidizing archaea
(AOA
Arch-amoAf (Francis et al., 2005) 95 °C 5 min; 45 cycles: 95 °C 30s,
53 °C 45s, 72 °C 45s, 78 °C 20s Arch-amoAr (Francis et al., 2005)
3.2.4 Soil physico-chemical analyses
Soil pH was measured in water suspensions at a solid-to-liquid ratio of 1:2.5. Hot water
extractable C (HWEC) and N (HWEN) analyses were determined according to Schulz (2004)
as well as Schulz and Korschens (1998). C content of hot water extracts was analyzed using
potassium dichromate in sulfuric acid milieu (Multi N/C analyzer, Analytik Jena, Jena,
Germany), while HWEN was analyzed by a modified Kjeldahl digestion (Hepburn 1908). Total
C (TC) and total N (Nt) of soils were quantified by dry combustion (Flash EA 1112 Elemental
Analyzer, Thermo Fisher Scientific). NH4+ and NO3
- were calorimetrically measured (ICRAF
1999). Soil sub-samples of 20 g each were used for moisture content (MC) determination by
weight loss after drying (105°C, 24 hours).
3.2.5 Statistical analysis
Data on abundance of the four studied genes and soil chemical properties were subjected to
analysis of variance (ANOVA) using Statistical Analysis Software program (SAS Institute
2014). For statistical analysis of the gene abundance data, a generalized linear mixed model
with a negative binomial distribution error and a log link function was used. The model was
fitted by restricted log pseudo-likelihood with expansion about the solution for the random
effects in the SAS GLIMMIX procedure. For the geochemical data, linear mixed models were
fitted in the SAS MIXED procedure. The data were checked for normality and
59
homoscedasticity on model residuals using quantile-quantile (Q-Q) plots, histograms and
studentized residual plots. Different transformations were tried and log base 10 selected as for
NH4+ and NO3
- in an attempt to satisfy the assumption underlying the ANOVA model (Piepho
2009). A full factorial split plot design model was specified with the effects of main factors
“Year” (years 2012 and 2013), “Growth stage” of maize (EC30, EC60) which was nested
within the season, “Organic input” (TD, CC, ZM, CON), “Mineral N fertilizer” (0 and 120 kg
N ha-1) and their interactions as fixed effects on the response variables (i.e., gene abundance,
geochemical properties). We accounted for the repeated measurements in the same plots by
fitting an error with a first order autoregressive covariance structure to the data. Statistical
significance of all effects was assessed at a significance level of P < 0.05. Blocks and blocks x
organic inputs interactions were considered as random effects and model selected accordingly
using the Akaike Information Criterion (AIC). Treatments means/medians were compared
using the PDIFF option of the LSMEANS as well as the letters (letter display) from the SAS
generalized linear mixed models procedure and SAS macro statement from the SAS linear
mixed models procedure for the abundance and geochemical data, respectively.
It is worth mentioning that no significant interactions between factors “Organic input” and
“Mineral N fertilizer” were determined for abundance of all four genes and soil chemical
properties data except for NH4+ (Table 3.2). Therefore, treatment means (i.e., CON, CC, ZM,
TD) are shown regardless of the mineral N fertilizer levels (i.e., 0 N, 120 N) and those of
mineral N levels regardless of organic input type (Fig. 3.2 and 3.3; Tables 3.3 and 3.4).
Pearson linear correlation analyses were conducted in the SAS COR procedure to relate the
abundance of the target genes (dependent variables) to the soil physico-chemical properties
(independent variables).
3.3 Results
3.3.1 Microbial abundance dynamics in response to organic input quality
Descriptive means (not subjected to ANOVA) of AOB and AOA abundance, which was given
as copy numbers of the respective amoA genes, are presented as supplementary information
(S3.6.1). AOB and AOA abundance revealed significant interactions between factors “Organic
input” and “Growth stage” (AOB, P < 0.01; AOA, P < 0.05, Table 3.2). AOB abundance
increased in year 2013 (1.90 x 108 gene copies g-1 dry soil) compared to 2012 (1.54 x 106 gene
copies g-1 dry soil) (P < 0.001) (Table 3.3). On the other hand, AOB abundance was lower in
EC60 than EC30 during both years (P < 0.001, Table 3.3). AOB abundance was higher in ZM
60
than CC, TD and CON at EC60 in 2012 (P < 0.05, Fig. 3.2a). In year 2013, TD had marginally
higher AOB abundance than CC during both growth stages of maize (P < 0.05, Fig. 3.2b).
In general, AOA abundance was not different between years 2012 and 2013 (P > 0.05, Table
3.3). Similar to AOB, AOA abundance was lower in EC60 than EC30 during both years (P <
0.05, Table 3.3). ZM increased AOA abundance compared to CON at EC30 in 2012 (P < 0.05,
Fig. 3.2c), while plots which received ZM and CC at EC30 as well as TD at EC60 in 2013 had
higher AOA abundance than CON (P < 0.05, Fig. 3.2d). Quality of organic inputs did not
influence AOA abundance during the study period (P > 0.05).
Descriptive means of bacterial and archaeal 16S rRNA genes abundance are presented as
supplementary information (S3.6.2). However, no significant interaction effects between all
four factors were determined for both 16S rRNA gene abundance (P > 0.05, Table 3.2).
Therefore, only the main effects of organic input quality, “Year” (seasonality) and “Growth
stage” on total prokaryotic communities are shown (Fig. 3.3, Table 3.3). Overall, bacterial 16S
rRNA gene abundance revealed 3.78 x 109 copies g-1 dry soil in 2012, but was low in 2013
amounting in average to 1.71 x 109 copies g-1 dry soil (P < 0.001, Table 3.3). Bacterial 16S
rRNA gene abundance remained relatively stable between EC30 and EC60 in 2012, while an
increase at EC60 was measured in 2013 (P < 0.05, Table 3.3). All plots which received organic
inputs had higher bacterial 16S rRNA gene abundance than CON (P < 0.05, Fig. 3.3a), while
quality of organic inputs had no effect (P > 0.05).
In contrast to total bacteria, total archaeal 16S rRNA gene abundance was low in 2012 (1.47 x
108 copies g-1 dry soil), but increased in 2013 (1.71 x 1010 copies g-1 dry soil) (P < 0.001, Table
3.3). Archaeal 16S rRNA gene abundance was high at EC60 in 2012 but low at EC60 in 2013
(P < 0.05, Table 3.3). Archaeal 16S rRNA gene abundance was lower in CON than ZM and
TD treatments (P < 0.05), while biochemical quality of organic inputs did not affect archaeal
16S rRNA gene abundance during the study period (P > 0.05, Fig. 3.3b).
61
Table 3.2: Statistical evaluation of gene abundance and soil chemical properties by full factorial ANOVA.
Source of
variation
Bacterial
16S
rRNA
gene
Archaeal
16S
rRNA
gene
Bacterial
amoA
gene
(AOB)
Archaeal
amoA
gene
(AOA)
Soil pH NH4+ NO3
- TC Nt HWEC HWEN MC
Units [copies g-
1 dry soil]
[copies g-
1 dry soil]
[copies g-
1 dry soil]
[copies g-
1 dry soil]
[mg kg-1] [mg kg-1] [%] [%] [mg kg-1] [mg kg-1] [%]
Organic
inputs (OI)
* n.s *** * * n.s n.s * ** ** ** **
Mineral
fertilizer(N)
* ** n.s ** *** ** *** n.s n.s n.s *** **
Year (Y) *** *** *** n.s n.s n.s *** n.s *** *** *** ***
Growth
stages (EC)
** ** *** *** *** *** *** *** *** *** *** ***
OI x N n.s n.s n.s n.s n.s * n.s n.s n.s n.s n.s n.s
OI x Y n.s n.s *** n.s n.s n.s n.s n.s n.s n.s n.s *
OI x EC n.s n.s *** * n.s * ** n.s n.s ** * *
N x Y n.s n.s n.s n.s n.s * * n.s n.s n.s n.s n.s
N x EC n.s n.s n.s n.s n.s ** *** n.s n.s n.s ** n.s
OI x N x EC n.s n.s n.s n.s n.s n.s n.s n.s n.s n.s n.s n.s
Factors: Organic inputs (TD, CC, ZM, CON); Mineral N fertilizer (0 kg N ha-1, 120 kg N ha-1); Year/season (years 2012 and 2013); Growth
stages of maize (EC30, EC60). Soil property definition: NH4+ = Ammonium, NO3
- = Nitrates, TC = Total carbon, Nt = Total nitrogen, HWEC =
Hot water extractable carbon, HWEN = Hot water extractable nitrogen, MC = Soil moisture content.
Significance levels: n.s: P > 0.05; *P < 0.05; **P < 0.01; ***P < 0.001.
62
Table 3.3: Abundance of the four assayed genes as influenced by crop growth stage, season and mineral N fertilizer. Values are means along with
standard errors. Total number of observations (n) = 96.
Effects of Total bacteria Total archaea Bacterial amoA (AOB) Archaeal amoA (AOA)
Factors
EC302012 3.98 ± 0.32 x 109a 1.29 ± 0.15 x 108d 1.85 ± 0.11 x 106c 7.43 ± 0.63 x 107a
EC602012 3.60 ± 0.29 x 109a 1.67 ± 0.19 x 108c 1.29 ± 0.09 x 106d 4.73 ± 0.40 x 107b
EC302013 1.44 ± 0.12 x 109c 1.95 ± 0.23 x 1010a 3.13 ± 0.07 x 108a 7.50 ± 0.64 x 107a
EC602013 2.02 ± 0.16 x 109b 1.50 ± 0.17 x 1010b 1.16 ± 0.03 x 108b 3.42 ± 0.29 x 107c
Year
Year2012 3.78 ± 0.25 x 109a 1.47 ± 0.15 x 108b 1.54 ± 0.08 x 106b 5.93 ± 0.44 x 107a
Year2013 1.71 ± 0.11 x 109b 1.71 ± 0.17 x 1010a 1.90 ± 0.04 x 108a 5.06 ± 0.38 x 107a
Mineral N
0 N 2.95 ± 0.24 x 109a 1.96 ± 0.24 x 109a 1.67 ± 0.07 x 107a 6.80 ± 0.61 x 107a
120 N 2.19 ± 0.18 x 109b 1.26 ± 0.16 x 109b 1.76 ± 0.07 x 107a 4.41 ± 0.40 x 107b
Factors: Growth stages of maize (young growth stage (EC30), flowering stage (EC60)); Year (years 2012 and 2013); Mineral N fertilizer (0 kg N
ha-1, 120 kg N ha-1). For each respective gene, abundance means values are an average of all organic treatments (CC, ZM, TD and CON) values
with and without mineral N for specific growth stage and specific year as labeled. Those of specific years under the effect “Year”, are an average
of all organic treatments both with and without mineral N regardless of crop growth stage, while those of mineral N are an average of all treatments
first, only treatments which did not receive mineral N (0 kg N ha-1) regardless of crop growth stage and year/season, and second an average of all
treatments which received (120 kg N ha-1) only during both years regardless of the crop growth stage and year/season. Different letters (a-d) against
the values (means) indicate treatments/groups with significant differences within each factor (P < 0.05).
63
Figure 3.2: Gene copy numbers of the ammonia-oxidizing genes in different treatments (Mean,
n = 6). Total number of observations = 96. (A) Bacterial amoA (AOB) gene in year 2012, (B)
AOB in year 2013, (C) Archaeal amoA (AOA) in year 2012, and (D) AOA in year 2013 after
application of biochemically contrasting organic inputs (4 Mg C ha-1 year-1). White bars =
young growth stage (EC30) of maize; Grey bars = flowering stage (EC60). Different letters (a-
c/A-D) above bars for the EC30 and EC60, respectively, indicate treatments with significant
differences (P < 0.05). Treatments codes: CON = Control, CC = Calliandra calothyrsus, ZM
= Zea mays, TD = Tithonia diversifolia.
64
Figure 3.3: Gene copy numbers of the total communities in different treatments (Mean, n =24).
Total number of observations = 96. (A) total bacteria (Bacterial 16S rRNA gene, (B) total
bacteria (Bacterial 16S rRNA gene) after application of biochemically contrasting organic
inputs (4 Mg C ha-1 year-1). Different letters (a-b) above bars indicate treatments with
significant differences (P < 0.05). Treatments codes: CON = Control, CC = Calliandra
calothyrsus, ZM = Zea mays, TD = Tithonia diversifolia.
3.3.2 Effect of mineral N fertilizer on microbial communities
Bacterial and archaeal 16S rRNA as well as AOB and AOA gene abundance showed no
interaction between factors “Organic input” and “Mineral N fertilizer” (P > 0.05, Table 3.2,
Fig. 3.2 and 3.3). Therefore, the main effects of mineral N (120 N) as opposed to the zero N
rates are shown (Table 3.3). Mineral N or its combination with biochemically contrasting
organic inputs reduced 16S rRNA gene abundance (bacteria, 26 %; archaea, 35 %) and also
AOA (35 %) compared to their counterparts without mineral N (P < 0.05). AOB abundance
remained unaffected upon combination of organic inputs with mineral N in comparison to zero
mineral N (P > 0.05).
3.3.3 Effect of input quality and mineral N fertilizer on soil physico-chemical properties
Soil pH, TC and Nt had no interactions between factors (P > 0.05, Table 3.2). Therefore, the
data of main effects of the factors are presented (Table 3.4). Their descriptive means are
however, presented as supplementary information (Table S3.6.1). CC had lower pH than TD
(P < 0.05). Application of mineral N reduced pH compared to the zero N rates (P < 0.01, Table
3.4). TC and Nt values were lower in CON than organic input treatments, but were not
influenced by mineral N fertilizer (P > 0.05, Table 3.4). Since the plots which received 0 or
120 kg N ha-1 had received organic inputs, it is likely that the organic inputs had strong effect
on Nt, masking the effect of mineral N which generally has a fast turnover, as was measured in
65
the inorganic and labile forms of N (Sądej, 2008). Stronger effect of organic inputs than mineral
N on Nt is in line with the fact that N in soils is mostly organic N. For NH4+, NO3
-, HWEC/N
and MC, interactions between factors were determined (P < 0.05, Table 3.2). Hence, their
individual results are provided (Tables 3.5 and 3.6). Quality of organic inputs did not influence
NH4+ contents (P > 0.05, Table 3.5). CON, CC and ZM combined with mineral N increased
NH4+ at EC60 in 2012 as well as ZM and TD at EC30 in 2013 compared to their counterparts
without mineral N (P < 0.05). Generally, NO3- contents were lower at EC60 in 2012. This was
linked to competition for nitrates from the crop as maize was tasseling. In addition, year 2012
had higher precipitation compared to 2013. Apparently, a heavy rainfall a night before the soil
sampling was conducted had been experienced which could have led to leaching of nitrates to
level below 0-15 cm we had considered in this study. TD and CC had higher NO3-
concentrations than CON and ZM at EC30 in 2013 (P < 0.05, Table 3.5). Application and
combination of mineral N with organic inputs increased NO3- contents at EC60 in both years
(P < 0.05). Soil moisture content was on average 6 % lower in 2013 than 2012 (P < 0.001). In
addition, it was lower at EC60 than EC30 in both years (P < 0.05, Table 3.5). TD at EC30 and
ZM at EC60 in 2012 had higher HWEC values than CC (P < 0.05, Table 3.6). Moreover, sole
TD had higher HWEC values than sole ZM at EC30 in 2013 (P < 0.05). Mineral N fertilizer
did not influence HWEC contents (P > 0.05). Organic input quality did not influence HWEN
values (P > 0.05, Table 3.6). HWEN values increased in TD treated plots at EC30, in CC treated
plots at EC60 in 2012 as well as in CON and CC treated plots at EC60 in 2013 (P < 0.05) upon
combination with mineral N fertilizer in comparison to their counterparts without mineral N
fertilizer.
66
Table 3.4: Soil chemical properties following amendment of soils with biochemically
contrasting organic inputs (4 Mg C ha-1 year-1) and mineral N fertilizer. Values are means for
soils sampled at young growth (EC30) and flowering (EC60) stages of maize crop during two
study seasons.
Soil chemical properties
Factors Soil pH TC [%] Nt [%]
Organic input treatments
CON 4.74c 2.27b 0.19b
CC 4.86bc 2.64a 0.22a
ZM 5.03ab 2.71a 0.22a
TD 5.13a 2.74a 0.23a
SED ± 0.086 ± 0.136 ± 0.008
Mineral N treatments
0 N 5.14a 2.60a 0.22a
120 N 4.74b 2.59a 0.22a
SED ± 0.031 ± 0.022 ± 0.001
Growth stages
EC302012 4.89b 2.69a 0.23a
EC602012 4.98a 2.52c 0.23a
EC302013 5.04a 2.59b 0.20c
EC602013 4.83b 2.57bc 0.22b
SED ± 0.036 ± 0.029 ± 0.002
Season
2012 4.93a 2.60a 0.23a
2013 4.94a 2.58a 0.21b
SED ± 0.031 ± 0.022 ± 0.001
Treatments: CON = Control, CC = Calliandra calothyrsus, ZM = Zea mays, TD = Tithonia
diversifolia. 0 N = 0 kg N ha-1; 120 N = 120 kg N ha-1. Soil property definition: TC = Total
carbon, Nt = Total nitrogen. Different letters (a-c) indicate treatments/groups means (least
square means (lsmeans)) with significant differences within each factor (P < 0.05).
67
Table 3.5: Ammonium (NH4+), nitrate (NO3
-) and soil moisture (MC) content following amendment of soils with biochemically contrasting
organic inputs (4 Mg C ha-1 year-1) and mineral N. Values are means/medians for soils sampled at young growth (EC30) and flowering (EC60)
stages of maize crop.
NH4+ [mg kg-1] NO3
- [mg kg-1] MC [%]
2012 2013 2012 2013 2012 2013
EC30 EC60 EC30 EC60 EC30 EC60 EC30 EC60 EC30 EC60 EC30 EC60
Treatments
CON 0 N 5.7b 1.2bcd 2.8bc 4.02a 20.4c 2.7b 1.9c 5.3b 39.3bc 33.6de 38.1b 24.4ab
120 N 7.1ab 2.6a 2.9bc 4.3a 25.6bc 17.0a 2.6bc 19.2a 38.4c 31.9e 37.9b 24.1abc
CC 0 N 9.5a 1.1cde 2.6bc 4.0a 29.1bc 3.3b 3.9ab 6.0b 40.3abc 35.9abc 40.3a 22.7c
120 N 8.1ab 1.7ab 3.3bc 3.9a 36.0abc 13.0a 4.6a 14.0a 40.2abc 35.2bcd 39.5ab 22.9bc
ZM 0 N 8.0ab 0.7e 3.6bc 3.6a 34.7abc 3.5b 2.1c 6.0b 41.7a 37.5a 40.8a 25.1a
120 N 8.6ab 1.7abc 5.2a 3.5a 48.4ab 11.1a 2.4bc 14.0a 40.2abc 36.5ab 40.0a 25.1a
TD 0 N 9.5a 1.0de 3.7ab 3.7a 36.3abc 3.6b 4.3a 6.9b 41.2ab 36.8ab 40.4a 24.8a
120 N 7.6ab 1.2bcd 2.5c 3.8a 79.6a 12.1a 4.6a 11.7a 40.8ab 34.0cde 39.3ab 24.0abc
SEDinputs ± 1.06
SEDnitrogen ± 1.03
Treatments codes: CON = Control, CC = Calliandra calothyrsus, ZM = Zea mays, TD = Tithonia diversifolia.
Different letters (a-d) against the values indicate treatments/groups with significant differences within each factor (P < 0.05). Properties without
SED values show the treatments (medians) after back transformation of the least square means (lsmeans) from analyses to the original scale.
68
Table 3.6: Hot water extractable carbon and nitrogen (HWEC/N) following amendment of soils with biochemically contrasting
organic inputs (4 Mg C ha-1 year-1) and mineral N. Values are means for soils sampled at young growth (EC30) and flowering
(EC60) stages of maize crop.
HWEC [mg kg-1] HWEN [mg kg-1]
2012 2013 2012 2013
EC30 EC60 EC30 EC60 EC30 EC60 EC30 EC60
Treatments
CON 0 N 350b 224d 432d 402c 25d 19b 14d 13c
120 N 277c 240cd 447dc 406bc 33cd 26ab 16cd 22ab
CC 0 N 364b 300bc 542ab 478ab 39bc 24bc 27ab 20b
120 N 363b 303bc 528ab 483a 46ab 31a 27ab 28a
ZM 0 N 403ab 378a 500bcd 549a 35dc 31ab 24ab 24ab
120 N 378b 332ab 513abc 538a 41bc 30a 22bc 28a
TD 0 N 464a 350ab 579a 526a 41bc 31ab 28a 23ab
120 N 454a 364ab 530ab 502a 53a 34a 26ab 27a
SEDinputs ± 33. ± 4.9
SEDnitrogen ± 29. ± 4.7
Treatments codes: CON = Control, CC = Calliandra calothyrsus, TD = Tithonia diversifolia, ZM = Zea mays.
Different letters (a-d) against the values indicate treatments/groups with significant differences within each factor (P < 0.05).
69
3.3.4 Correlation between soil physico-chemical properties and microbial abundance
AOA abundance was positively correlated with all soil chemical properties, except NO3- (i.e.,
soil pH (r = 0.549), TC (r = 0.526), MC (r = 0.520) (P < 0.001), Nt (r =0.336), HWEN (r =
0.299) (P < 0.01) as well as NH4+ (r = 0.264) and HWEC (r = 0.247) (P < 0.05)). On the other
hand, AOB abundance was negatively correlated with HWEN (r = -0.454), NO3- (r = -0.451),
Nt (r = -0.424) (P < 0.001) and NH4+ (r = -0.205; P < 0.05), but positively correlated with
HWEC (r = 0.676; P < 0.001). Bacterial 16S rRNA gene abundance was positively correlated
with Nt (r = 0.635), HWEN (r = 0.498), soil pH (r = 0.485), TC (r = 0.483) (P < 0.001) as well
as NO3- (r = 0.255) and MC (r = 0.264) (P < 0.05). Archaeal 16S rRNA gene abundance
revealed positive correlations with HWEC (r =0.797; P < 0.001), soil pH (r = 0.271) and TC
(r = 0.208) (P < 0.05), but it was negatively correlated with NO3- (r = -0.451), HWEN (r = -
0.411) (P < 0.001) as well as MC (r = -0.255; P < 0.05).
3.4 Discussion
3.4.1 AOB and AOA abundance respond conversely to organic input quality
In agreement with our first hypothesis, we determined a promotion of AOB abundance under
the influence of high quality TD inputs compared to intermediate quality CC inputs in 2013.
The observed promotion of AOB abundance by TD was attributed to the high and available
organic N compared to high polyphenols and lignin contents in CC which induce an organic N
stress situation through the formation of polyphenol-protein bonds limiting N access by soil
microbial communities (Millar and Baggs 2004; Rasche et al. 2014). This finding was partially
supported by negative correlations of AOB abundance with HWEN and Nt contents suggesting
a dependence of AOB on organic input derived N as energy source as indicated by strong
organic input effects on AOB in this study (Millar and Baggs 2004; Musyoki et al. 2015).
On the other hand, the hypothesized delayed N release from intermediate quality inputs (CC)
was not confirmed by the available data of AOB abundance during both years. At the same
time, NH4+ concentration in the soil remained not influenced by quality of organic inputs. This
meant that the mineralized N (NH4+) from the high quality inputs was immobilized by
microbial communities or taken up by plants outcompeting its sorption in the soil (Chivenge et
al. 2009; Muema et al. 2015). It has been shown for CC its high polyphenol and N contents
(Gentile et al. 2011b) often lead to a strong sequestration (complexation) of organic N (e.g.,
proteins) by polyphenols (Millar and Baggs 2004; Mutabaruka et al. 2007; Schmidt et al. 2013).
Accordingly, we suggested that such protein-polyphenol-complexes were still existent at EC60
70
delaying protein degradation (Rasche et al. 2014) as precursor of NH4+ provision. This might
have explained the inexistent promotion of AOB abundance. A similar effect of polyphenol-
protein complexes was reported by Kamolmanit et al. (2013) who observed that the provision
of organic N from intermediate quality inputs of Tamarindus indica was postponed to later
decomposition stages when polyphenol oxidizing microorganisms (e.g., fungi) were stimulated
and started to degrade protein-polyphenol complexes. Therefore, it was suggested that CC
inputs require a prolonged period during the growing season to be fully decomposed.
Low quality input (i.e., ZM) in comparison to its intermediate quality counterpart (i.e., CC)
induced an increase of AOB abundance from EC30 to EC60. A similar trend was observed for
total bacteria. This confirmed our hypothesis on the gradual decomposition of low quality
inputs over the season (Partey et al. 2013). Accordingly, the slow release of cellulose derived
metabolites was presumably controlled by physico-chemical protection of cellulose by lignin
impairing its fast and early decomposition (Talbot et al. 2012; Kunlanit et al. 2014).
Generally, AOB abundance tended to reduce at EC60 regardless of input quality. This was
linked to competition for nutrients by the maize crop at EC60 for reproduction compared to
EC30 when the crops are mainly elongating (Onwonga et al., 2010). A similar finding was
found by Hai et al., 2009 who linked such a reduction of microbial communities at EC60 to
soil moisture limitation, a trend we also observed in our study.
Conversely to AOB, AOA abundance remained unaffected by organic input quality. This
finding was in agreement with the fact that quality of organic inputs did not influence NH4+
and Nt concentrations in the soil. We explained this by the strong soil organic carbon (SOC)
background of this clayey soil (Gentile et al. 2011a), offering sufficient, labile and easily
degradable C substrates to AOA (Stopnišek et al. 2010). In addition, AOA were shown to
acquire a diverse array of resources existent in all tested organic input treatments. These
arguments were partially corroborated by positive correlations of assayed soil chemical
properties (e.g., TC, HWEC, HWEN, Nt, NH4+) with AOA abundance. Although it was
reported that AOA have a strong affinity for NH4+ (Valentine 2007; Schleper 2010; Levičnik-
Höfferle et al. 2012), they can also explore alternative N sources (e.g., soil organic N pool).
AOA were shown also to be unaffected by changes in NH4+ concentrations (Jia and Conrad
2009) and after exposure to organic inputs including biochemically contrasting sorghum straw
and cattle manure (Hai et al. 2009).
The effect of organic input quality on AOB and AOA abundance and total prokaryotic
communities was partially masked by climatic differences between the two study years and
seasons. Although the obtained results in general confirmed our hypotheses, we noticed a
71
season induced inconsistency which corroborated the common difficulty of deriving conclusive
interpretations when analyzing single seasons (Hai et al. 2009; Wessén et al. 2010).
Accordingly, rainfall dependent soil moisture alterations exposed distinct effects on abundance
of studied genes. While total bacterial abundance decreased in 2013, we observed the opposite
for total archaea. This suggested total archaea to be more tolerant to low soil moisture
conditions as negative correlations were observed. Reduction in soil moisture content also
matched decreased Nt and HWEN. This was particularly evident in 2013 when limited organic
N availability induced a decrease of total bacterial abundance. Contrastingly, total archaea
proliferated despite the prevailing low soil moisture or substrate conditions. This supported
their hypothesized adaptation potential to chronic energy stress (Valentine 2007).
The low AOB abundance in 2012 corroborated the findings of Muema et al. (2015) obtained
from the same soils obtained during a dry spell (March, 2012) before incorporation of organic
inputs. In our study, this dry spell before the start of long season rains was more severe in year
2012 than 2013 (Fig. 3.1). Consequently, soil moisture limited the availability of substrates to
AOB, which are commonly sensitive to nutrient availability (Levičnik-Höfferle et al. 2012). It
was suggested that AOB had not yet fully recovered from the effects of substrate limitation
during the dry spell most likely explaining their low abundance during the rainy period in 2012
(Stark and Firestone 1995).
High precipitation in 2012 that was received mainly with proceeding cropping period induced
a more positive water balance than year 2013 (data not shown). In addition, an average
approximate water filled pore space (WFPS) of 80 % was determined for year 2012 compared
to 60 % in 2013 (Gleeson et al. 2010). Gleeson et al. (2010) suggested 65 % to be the optimum
WFPS for proliferation and activities of AOB. Furthermore, Avrahami and Bohannan (2007),
Szukics et al. (2010) and Gleeson et al. (2010) reported AOB as more sensitive to WFPS
alterations than AOA. This meant that the high soil moisture during the cropping season in
2012 limited the oxygen availability for AOB, which were shown to be more sensitive to
oxygen concentration than AOA (Morimoto et al. 2011; Zhalnina et al. 2012). This clarified
the lack of response of AOB abundance to quality of organic inputs particularly at early maize
growth stage in year 2012 as well as their increased abundance in year 2013. Consequently, it
could be suggested that influence of precipitation on oxygen concentrations in soil solution
(Morimoto et al. 2011; Zhalnina et al. 2012), WFPS and the organic input driven N substrate
availability to microbial communities contributed to a niche differentiation between AOB and
AOA. The interactions between factors “Year/season” (e.g., soil moisture) and “Organic input
quality”, both of which dependently regulated the dynamics of AOB and AOA abundance,
72
necessitates however, further research to illuminate precisely microbial dynamics with respect
to biochemical quality of organic inputs. In this respect, the noticed climatic variation between
the two study seasons, which partially masked the hypothesized effects of organic input quality,
particularly on archaeal communities (i.e., AOA and total archaea), clearly substantiates the
need for a longer and consecutive study period than considered in our study.
3.4.2 Effect of mineral N fertilizer on dynamics of soil microbial communities
Decreased abundance due to application of mineral N fertilizer or its combination with organic
inputs as opposed to its zero rates was observed for AOA as well as for total bacteria and
archaea. We partially explained these findings with relatively higher pH values observed in the
plots without mineral N fertilizer as opposed to those treated with fertilizer N (Hallin et al.
2009). Accordingly, the observed reduction of gene abundances was attributed to the indirect
effect of NH4+ fertilizer application reducing soil pH (Hallin et al. 2009; He et al. 2007) and
thereby suppressing the proliferation of soil microbial communities including AOA (Liu et al.
2004; Hatzenpichler et al. 2008; Onwonga et al. 2010; Geisseler and Scow 2014).Contrarily,
Hallin et al. (2009) reported a promotion of AOA abundance following an increased soil pH
with application of calcium nitrate fertilizer as opposed to NH4+ fertilizer. It could thus be
postulated that fertilizer type dependent soil pH alterations had contrasting effects on AOA
abundance. However, other studies contradicted our observations by reporting the dominance
of AOA in acidic soils due to their high affinity for NH4+ compared to AOB (Gubry-Rangin et
al., 2010; Martens-Habbena et al., 2009; Nicol et al., 2008). Therefore, an alternative
explanation to the reduced AOA abundance in relation to use of inorganic N fertilizer was the
chaotropic effect of NH4+ and NO3
- components in the fertilizer (Hallsworth et al. 2003; Cray
et al. 2015; de Lima Alves et al. 2015). High NH4+ concentration in soils was reported to inhibit
AOA nitrification (Di et al. 2009; Tourna et al. 2010). Specifically under reduced water
availability, a chaotropic situation inhibiting microbial growth was thus likely to have occurred
(Cray et al. 2013; Stevenson et al. 2014; Stevenson and Hallsworth 2014).
Conversely to AOA, AOB abundance remained unaffected by mineral N addition, and further
there was no interaction found between factors “Organic input” and “Mineral N fertilizer”. It
is therefore proposed that AOB utilized both organic and inorganic N while AOA might have
mainly utilized organic N. Similar findings were provided by Wessén et al. (2010) who did not
observe any interaction between organic inputs (e.g., cereal straw, peat) and mineral N applied
as calcium nitrate on AOB abundance. This presumed stability of AOB towards NH4+ fertilizer
73
additions indirectly indicates AOB being less sensitive to N fertilizer induced pH changes than
AOA. This finding supports the acknowledged AOB dominated nitrification under mineral
fertilizer conditions (Jia and Conrad 2009; Di et al. 2009, Di et al. 2010). This implied that
chaotropism effect of NH4+ and NO3
- compounds on AOB was balanced by its adaptation to
high concentrations of readily available nutrients (Jia and Conrad 2009; Zhalnina et al. 2012;
de Gannes et al. 2014).
3.5 Conclusions
Our 2-year study in the SOM field experiment allowed us to corroborate the regulating effect
of contrasting biochemical quality of organic inputs on soil prokaryotic communities. We
observed that particularly AOB revealed a sensitive biochemical quality dependent response,
while AOA remained largely unaffected. On the other hand, sole use of mineral N fertilizer or
its combination with organic inputs, regardless of their biochemical quality, decreased
abundance of AOA, total bacteria and archaea, but not that of AOB. We presumed that this
discrepant response was mostly controlled by seasonality dependent soil moisture alternations.
Hence, future studies should consider the determination of soil water availability throughout
seasons to ascertain the explicit chaotropic effect of inorganic fertilizer components (i.e., NH4+
and NO3-) on the proliferation and activities of archaeal and bacterial ammonia-oxidizing
communities. In this respect, we recommend rRNA-based studies along with nucleic acid
stable isotope probing (España et al., 2011) to demarcate those community members which are
particularly active at defined time points during a cropping season. This will generate a more
conclusive understanding about the resource- and soil climate-dependent niche differentiation
between active AOB and AOA communities in agricultural soils.
74
3.6 Supplement
Supplement S3.6.1: Gene copy numbers of the ammonia-oxidizing genes in different
treatments (descriptive means ± standard deviation, n=3). Total number of observations = 96.
(A) bacterial amoA (AOB) gene in year 2012, (B) AOB in year 2013, (C) archaea amoA (AOA)
in year 2012, and (D) AOA in year 2013 after application of biochemically contrasting organic
inputs (4 Mg C ha-1 year-1) and combination with mineral N. White bars = young growth stage
(EC30) of maize; Grey bars = Flowering stage (EC60) of maize. Treatments codes: Organic
inputs; CON = Control, CC = Calliandra calothyrsus, ZM = Zea mays, TD = Tithonia
diversifolia. Mineral N; 0 N = 0 kg N ha-1, 120 N = 120 kg N ha-1.
75
Supplement S3.6.2: Gene copy numbers of the total communities in different treatments
(descriptive means ± standard deviation, n=3). Total number of observations = 96. (A) total
bacteria (Bacterial 16S rRNA gene) in year 2012, (B) total bacteria (Bacterial 16S rRNA gene)
in year 2013, (C) total archaea (Archaeal 16S rRNA gene) in year 2012, and (D) total archaea
(Archaeal 16S rRNA gene) in year 2013 after application of biochemically contrasting organic
inputs (4 Mg C ha-1 year-1) and combination with mineral N. White bars = young growth stage
(EC30) of maize; Grey bars = flowering stage (EC60) of maize. Treatments codes: Organic
inputs; CON = Control, CC = Calliandra calothyrsus, ZM = Zea mays, TD = Tithonia
diversifolia. Mineral N; 0 N = 0 kg N ha-1, 120 N = 120 kg N ha-1.
76
Table S3.6.1: Soil pH, total carbon (TC) and total nitrogen (Nt) content following amendment of soils with biochemically contrasting organic
inputs (4 Mg C ha-1 year-1) and mineral N. Values are descriptive means ± standard deviations for soils sampled at young growth (EC30) and
flowering (EC60) stages of maize crop. Total number of observations = 96.
Treatments codes: CON = Control, CC = Calliandra calothyrsus, ZM = Zea mays, TD = Tithonia diversifolia, 0 N = 0 kg N ha-1, 120 N = 120
kg N ha-1.
Soil pH TC [%] Nt [%]
2012 2013 2012 2013 2012 2013
Treatments EC30 EC60 EC30 EC60 EC30 EC60 EC30 EC60 EC30 EC60 EC30 EC60
CON 0 N 4.88 ±
0.17
4.94 ±
0.12
5.03 ±
0.13
4.87 ±
0.12
2.44
±0.10
2.24 ±
0.17
2.27 ±
0.10
2.25 ±
0.12
0.21 ±
0.004
0.20
±0.012
0.18 ±
0.009
0.18 ±
0.012
CON 120 N 4.49 ±
0.10
4.55 ±
0.11
4.70 ±
0.09
4.46 ±
0.13
2.31 ±
0.10
2.19 ±
0.08
2.23 ±
0.03
2.23 ±
0.09
0.20 ±
0.011
0.20 ±
0.007
0.17 ±
0.007
0.19 ±
0.003
CC 0 N 4.96 ±
0.11
5.06 ±
0.25
5.09 ±
0.26
4.93 ±
0.30
2.71 ±
0.24
2.56 ±
0.21
2.67 ±
0.31
2.65 ±
0.27
0.24 ±
0.016
0.24 ±
0.015
0.21 ±
0.020
0.23 ±
0.016
CC 120 N 4.70 ±
0.19
4.74 ±
0.18
4.86 ±
0.30
4.55 ±
0.23
2.83 ±
0.14
2.51 ±
0.24
2.62 ±
0.18
2.62 ±
0.26
0.25
±0.013
0.23 ±
0.020
0.21 ±
0.016
0.23 ±
0.020
ZM 0 N 5.25 ±
0.32
5.38 ±
0.30
5.40 ±
0.26
5.08 ±
0.36
2.82 ±
0.27
2.60 ±
0.34
2.71 ±
0.43
2.75 ±
0.36
0.24 ±
0.015
0.24 ±
0.028
0.21 ±
0.030
0.23 ±
0.024
ZM 120 N 4.75 ±
0.20
4.90 ±
0.16
4.94 ±
0.16
4.57 ±
0.13
2.66 ±
0.30
2.67 ±
0.32
2.79 ±
0.38
2.74 ±
0.18
0.23 ±
0.011
0.23 ±
0.021
0.21 ±
0.022
0.23 ±
0.012
TD 0 N 5.35 ±
0.31
5.40 ±
0.26
5.40 ±
0.11
5.28 ±
0.35
2.88 ±
0.12
2.66 ±
0.21
2.73 ±
0.10
2.71 ±
0.22
0.25 ±
0.005
0.24 ±
0.015
0.21 ±
0.008
0.23 ±
0.020
TD 120 N 4.77 ±
0.16
4.94 ±
0.12
4.96 ±
0.14
4.92 ±
0.08
2.86 ±
0.23
2.73 ±
0.13
2.71 ±
0.19
2.65 ±
0.09
0.25 ±
0.018
0.25 ±
0.012
0.22 ±
0.014
0.23 ±
0.013
77
Chapter 4 Soil texture interrelations with organic inputs quality contrastingly influence
abundance and composition of ammonia-oxidizing prokaryotes in a tropical arable soil
Abstract
Interrelations of soil texture and organic inputs quality influence the availability of organic
nutrients. This was assumed to distinctly shape the dynamics of ammonia-oxidizing bacterial
(AOB) and archaeal (AOA) communities. We tested two contrasting soils (i.e., clayey Humic
Nitisol and sandy Ferric Alisol) of soil organic matter (SOM) long-term trials established in
Kenya in 2002. Since 2002, soils were continuously treated (4 Mg C ha-1 year-1) with
biochemically contrasting organic inputs including Tithonia diversifolia (TD; C/N ratio: 13,
lignin: 8.9 %, polyphenols: 1.7 %), Calliandra calothyrsus (CC; 13; 13; 9.4) and Zea mays
stover (ZM; 59; 5.4; 1.2). In 2013, soils (0-15 cm) were sampled at young growth (EC30) and
flowering (EC60) stages of maize and subjected to DNA-based community analysis (amoA
gene quantification and community composition). Generally, soil texture was a major
determinant of the abundance and composition of assayed communities (i.e., AOB, AOA, total
bacteria and total archaea). In contrast, organic input quality primarily affected bacterial
communities. Clayey compared to sandy soil revealed higher AOB and AOA abundance
regardless of organic input quality. This was explained by the larger soil organic C background
in the clayey soil promoting AOB and AOA proliferation. In the sandy soil, N-rich TD and
cellulose-rich ZM promoted AOB abundance compared to CC. This AOB depression under
CC was linked to the high contents of polyphenols and lignin in CC inducing a considerable C
and N stress, inhibiting AOB’s growth during the early stages of decomposition. In the clayey
soil, TD versus ZM and CC versus ZM revealed separations of AOB composition. This implied
that AOB was able to access N in CC and that N availability was the driving force for AOB
alterations provided that C substrates were available. AOA composition revealed differences
between TD versus CC and CC versus ZM in the sandy soil. This suggested that AOA
alterations are realized mainly when C and N substrates are simultaneously limiting. Overall,
the effects of interrelations between soil texture and organic input quality were specific to
functionally relevant microbial groups with AOB being more responsive than AOA. As this
work was based on functional potential of microbial communities using DNA-based analyses,
RNA-based approach is recommended to capture those communities that are actively involved
in nitrification process.
78
Keywords: Clayey and Sandy texture; Organic input quality; Ammonia-oxidizing bacteria and
archaea; Microbial abundance; community composition.
4.1 Introduction
Tropical agro-ecosystems are generally resource limited justifying the use of organic inputs in
complementation of mineral fertilizers to replenish soil nutrients (e.g., nitrogen (N)) for crop
growth (Chivenge et al., 2009; Mtambanengwe et al., 2006; Rasche and Cadisch, 2013). Small-
holder farmers can rely on a broad array of organic inputs (e.g., crop residues, prunings from
shrubs and trees) differing in their biochemical quality. Organic input quality is commonly
defined by the contents of N, lignin, polyphenols and also cellulose (Kunlanit et al., 2014; Palm
et al., 2001a). In consequence, it was acknowledged that decomposition and mineralization
activities of soil microorganisms, which play a key role in providing organically bound
nutrients to crops, are critically regulated by organic input quality (Rasche and Cadisch, 2013).
In this respect, organic inputs such as Tithonia diversifolia (TD; C/N ratio: 13, Lignin: 8.9 %;
Polyphenols: 1.7 %) accelerated decomposition availing N early in the cropping season, while
other resources such as Calliandra calothyrsus (CC; 13; 13; 9.4) showed delayed
decomposition patterns (Palm et al., 2001b). The delayed decomposition of CC was associated
with the formation of polyphenol protein bonds limiting the accessibility of organic N by
microbial decomposers (Millar and Baggs, 2004). Recently, it was shown that such polyphenol-
induced N limitation prompted a critical stress response through increase in abundance of
proteolytic bacteria in sandy agricultural soils (Rasche et al., 2014).
However, it remains yet to be fully understood to which extent microbial driven nitrification is
regulated by organic input quality. This may count for both nitrifying archaea (AOA) and
bacteria (AOB) whose community dynamics are generally assessed on the basis of the
functional marker amoA gene (α-subunit of the enzyme ammonia-monooxygenase) (Prosser
and Nicol, 2008; Zhang et al., 2013). Up to date, existing observations of organic input quality
effects on AOA and AOB community dynamics have not displayed clear response patterns.
For example, low C/N ratios, hence easily decomposable organic materials (e.g., Brassica
napus, cereal straw) in comparison to high C/N ratio inputs (e.g., compost, peat) with a gradual
decomposition pattern promoted the abundance of AOA in both sandy and clay loam soils
(Strauss et al., 2014; Wessén et al. 2010). Conversely, no treatment effect was observed on
AOB abundance. Moreover, Hai et al. (2009) found a promotion of AOA and AOB abundance
79
in sandy soils after treatment with nutrient rich semi-decomposed cattle manure (low C/N
ratio), while both groups were decreased by sorghum straw with a high C/N ratio. On the other
hand, Wessén et al. (2010) observed a clear community composition differentiation of both,
AOB and AOA in a clay loam soil treated with biochemically distinct organic inputs.
These contradictory results clearly imply that the response of nitrifying communities to organic
input quality may be also determined by other factors including physico-chemical soil
characteristics (Kögel-Knabner et al., 2008). Clayey compared to coarse textured sandy soils
show a generally higher accumulation potential of soil organic carbon (SOC) either through
physical protection of freshly added organic inputs or via organo-mineral associations
(Dieckow et al., 2009; Kögel-Knabner et al., 2008). In consequence, these characteristics of
fine-textured clayey soils induced a higher abundance of total bacteria than in sandy soils. This
was attributed to the higher surface area of clay particles promoting bacterial growth as well as
providing protection from predation (Kögel-Knabner et al., 2008; Neumann et al., 2013; Poll
et al., 2003). With respect to nitrifying soil microorganisms, Pereira e Silva et al. (2012)
reported a higher abundance of AOB and AOA in clayey than in sandy soils, supporting the
mechanisms of clayey soil linked promotion of microbial populations (Kögel-Knabner et al.,
2008). Other observations evidenced that AOA in fine textured soils preferentially utilize
native SOC (Stopnišek et al., 2010; Zhalnina et al., 2012; Zhang et al., 2010) explaining their
increased abundance over sandy soils. In contrast, Wessén et al. (2011) found AOA abundance
to be negatively correlated with clay content, which supported its general adaptation to nutrient
limiting conditions (Levičnik-Höfferle et al., 2012; Valentine, 2007). In conclusion, it remains
speculative if organic inputs of similar biochemical quality induce consistent responses of
nitrifying communities in soils with distinct textures.
Our objective was therefore to determine the interrelations between soil texture and organic
inputs of contrasting biochemical quality on the abundance and community composition of
AOB and AOA in tropical agricultural soils. We hypothesized a higher AOB and AOA
abundance in clayey than sandy soils regardless of organic input quality. This appears justified
by the strong SOC background of clayey soils as determined by the acknowledged beneficial
characteristics of clay minerals in sequestering organic substrates fostering microbial
proliferation (Gentile et al., 2011a; Kögel-Knabner et al., 2008). Furthermore, alteration of
community composition, i.e. AOB and AOA is proposed in clayey compared to sandy soil due
to its high nutrients status promoting community alterations. Consequently, we argue that the
general resource limitation in sandy soils is compensated with the addition of high quality
organic inputs (low C/N) availing high amounts of organic C and N to AOB and AOA early in
80
the season compared to intermediate quality inputs due to high polyphenols and lignin contents
and low quality inputs (high C/N) limiting faster decomposition. In this respect, we
hypothesized a promotion of AOB and AOA abundance and community composition alteration
in sandy soils later in the season as a consequence of gradual decomposition of intermediate
and low quality inputs availing C and N substrates to the communities.
4.2 Materials and methods
4.2.1 Sites description and experimental design
The study was carried out at the soil organic matter (SOM) long-term field experiments in
Embu (0˚ 30' S, 37˚ 30' E) and Machanga (0˚ 47' S, 37˚ 40' E) which were established in 2002.
Embu site (1380 m above sea level (a.s.l)) is located in Embu district in the central highlands
of Kenya (130 km northeast of Nairobi). Machanga site (1060 a.s.l) is located in Mbeere
district, approximately 200 km northeast of Nairobi. Embu site has an annual mean temperature
of 20 °C and a mean annual rainfall of 1200 mm, while Machanga site is characterized by
frequent droughts due to erratic and unreliable rains with a mean annual temperature of 26 °C
and an average annual rainfall of 900 mm. Rainfall at both sites occurs bimodal with long rains
received from mid-March to June and short rains from mid-October to December. The soil at
Embu is defined as a Humic Nitisol (FAO, 2006) dominated by kaolinite minerals derived from
basic volcanic rocks. The texture of the topsoil layer (0-15 cm) was characterized by 17 %
sand, 18 % silt and 65 % clay and contained 29.3 g kg-1 organic C, 2.8 g kg-1 total N and a pH
of 5.8 (H2O) at the start of the experiment (Chivenge et al., 2009; Gentile et al., 2011a).
Machanga site is characterized by a sandy soil derived from granitic gneisses and is classified
as Ferric Alisols (FAO, 2006). The texture of the top layer (0-15 cm) contained 67 % sand, 11
% silt and 22 % clay with 5.3 g kg-1 organic C, 0.6 g kg-1 total N and a pH (H2O) of 6.1 at the
onset of the experiment (Gentile et al., 2011a). Moreover, Machanga soil was reported to be
deficient in major crop nutrients such as N and P (Gentile et al., 2011a), while the Embu soil
was considered fertile with greater total carbon and nitrogen (TC, Nt), as well as higher
exchangeable bases measured at the onset of both experiments (Chivenge et al., 2009; Gentile
et al., 2011a).
The two SOM long-term field experiments were implemented to primarily study the effect of
continuous annual application of biochemically contrasting organic inputs and their
combination with mineral N fertilizer on the performance of Zea mays as well as soil C and N
dynamics (Chivenge et al. 2009, 2011; Gentile et al. 2011a; Rasche et al., 2014). This presented
81
work however, focused only on organic input treatments excluding the effect of fertilizer N by
not considering the plots that received mineral N fertilizer. The experiment was laid out in a
randomized complete block design (RCBD) with three replicates with plot sizes of 6 m x 5 m
and 6 m x 6 m in the clayey and sandy soil respectively. Further details of the field experiment
set-up can be retrieved from Chivenge et al. (2009) and Gentile et al. (2009). Three organic
input types were considered in this study: high quality Tithonia diversifolia (C/N ratio: 13,
lignin: 8.9 %; polyphenols: 1.7 %), intermediate quality Calliandra calothyrsus (13; 13 %; 9.4
%) and low quality Zea mays (59; 5.4 %; 1.2 %) (Gentile et al., 2011b). At the onset of long
rains in each year, these organic inputs (leaves, petioles and small branches for C. calothyrsus
in addition to stems for T. diversifolia and Z. mays stover) were collected and analyzed for dry
matter and total C and N content. Dry matter and total C data were then used to determine the
amount of each organic material to be applied at 4 Mg C ha-1 a-1 (Chivenge et al. 2009; Gentile
et al. 2011a). Prior to maize (Zea mays) sowing, organic inputs were chopped into small pieces,
broadcast and hand-incorporated at a soil depth of 0-15 cm (Chivenge et al. 2009; Gentile et
al. 2011a, 2011b). All plots received a blanket basal application of 60 kg P ha-1 season-1 and 60
kg K ha-1 season-1 before sowing.
4.2.2 Soil sampling
In April 2013, at young maize growth stage (EC30) and early June at flowering stage (EC60)
(Milling et al., 2005), soil samples were collected from the no input control (CON), C.
calothyrsus (CC), Z. mays (ZM) and T. diversifolia (TD) treatments. In each plot, 10 soil sub-
samples were randomly obtained at a depth of 0-15 cm using a soil auger. These 10 sub-samples
were bulked to one composite sample per plot, sieved (2-mm mesh) and stored at 4°C. In total,
24 soil samples were obtained per site. Prior to analysis, each composite soil sample was split
into three proportions. A fresh sample was used for immediate extraction of ammonium (NH4+)
and nitrate (NO3-). The second proportion was air-dried for analysis of pH, hot water
extractable C and N (HWEC/N), total C (TC) and total N (Nt). The third proportion was freeze-
dried to be shipped to Germany for microbial community analyses.
4.2.3 Microbiological soil analysis
4.2.3.1 Soil DNA extraction
Total soil DNA was isolated from 0.5 g of each freeze dried soil sample using the FastDNA TM
SPIN Kit for Soil (MP Biomedicals, Solon, Ohio, USA) according to the manufacturer’s
instructions. Quality of isolated DNA was checked on 1.5 % (w/v) agarose gels. DNA was
82
quantified photometrically (NanoDrop™ 2000/2000c spectrophotometer, Thermo Fisher
Scientific, Waltham, MA, USA). For each DNA sample, two consistent NanoDrop readings,
whose variation was less than 5 %, were considered for subsequent determination of DNA
concentration. Quantified DNA was stored at -20 °C for further analysis.
4.2.3.2 Microbial abundance
For DNA-based quantification of target genes (total bacteria and archaea (16S rRNA gene) as
well as ammonia-oxidizing bacteria (bacterial amoA gene; AOB) and archaea (archaeal amoA
gene; AOA)), plasmid standards were prepared (Table 4.1), purified (Invisorb Fragment
CleanUp kit, Stratec Molecular GmbH, Berlin, Germany) and quantified according to Rasche
et al. (2011). The qPCRs (three analytical replicates per DNA sample) were conducted in a 25
µl reaction cocktail containing 12.5 µl Power SYBR green master mix (Applied Biosystems,
Foster City, CA, USA), 0.4 µM of each primer (Table 1), 0.25 µl T4 gene 32 protein (500 μg
ml-1, MP Biomedicals) and 5 ng DNA template (except for AOB gene (10 ng)) from the prior
prepared DNA working stock of 5 ng μl-1. The final dilution of DNA extracts was optimized
in advance to avoid any reaction inhibition by humic acids co-extracted during DNA isolation
(data not shown). Reactions were run on a StepOne Plus Real-time PCR detection system
(Applied Biosystems) starting with an initial denaturation step at 95 °C for 10 min, followed
by amplification cycles specific for each target gene (Table 4.1). Melting curve analysis of
amplicons was conducted to ensure that fluorescence signals originated from specific
amplicons and not from primer dimers or other artifacts. Gene copy numbers and reaction
efficiencies (total bacteria 102 % ± 1, total archaea 85 % ± 14, AOB 97 % ± 11, and AOA 86
% ± 5) were calculated using StepOne Software version 2.2.2 (Applied Biosystems) and
presented per gram of dry soil.
4.2.3.3 Microbial community composition
For terminal restriction fragment length polymorphism (TRFLP) analysis, bacterial amoA
(AOB) and archaeal amoA (AOA) genes were amplified as previously described (Rasche et al.,
2011) (Table 4.1). All forward primers were labeled with 6-carboxyflourescein at their 5’ ends.
Amplicons were purified (Invisorb Fragment CleanUp kit) prior to digestion. Two hundred ng
of each purified amplicon were digested with a 5 U combination of AluI and RsaI (New
England Biolabs (NEB), Ipswich, USA) and the reactions incubated at 37 °C for 4 hours.
Digested products were purified (Sephadex G-50, GE Healthcare Biosciences, Waukesha, WI,
USA) (Rasche et al., 2006) and an aliquot of 2 µl was mixed with 17.75 µl HiDi formamide
83
(Applied Biosystems) and 0.25 µl internal 500 ROX™ size standard (Applied Biosystems).
Labeled terminal-restriction fragments (T-RFs) were denatured at 95 °C for 3 min, chilled on
ice and detected on an ABI 3130 automatic DNA sequencer (Applied Biosystems). Peak
Scanner™ software package (version 1.0, Applied Biosystems) was used to compare relative
lengths of T-RFs with the internal size standard and to compile electropherograms into numeric
data set, in which fragment length and peak height >50 fluorescence units were used for profile
comparison. TRFLP profiles used for statistical analyses were normalized according to Dunbar
et al. (2001).
84
Table 4.1: Description of primer sets, PCR ingredients and amplification details used for quantitative PCR (qPCR) and TRFLP analyses.
Target gene Primer (reference) qPCR TRFLP
All bacteria Eub338f (Lane, 1991) 95 °C 5 min; 40 cycles: 95 °C 30s,
Eub518r (Muyzer et al., 1993) 55 °C 35s, 72 °C 45s
All archaea Ar109f (Lueders and Friedrich, 2000) 95 °C 5 min; 40 cycles: 95 °C 30s,
Ar912r (Lueders and Friedrich, 2000) 52 °C 35s, 72 °C 45s, 78 °C 20s
Ammonia-oxidizing AmoA-1f (Rotthauwe et al., 1997) 95 °C 5 min; 45 cycles: 95 °C 30s, 95 °C 5 min; 40 cycles: 94 °C 30s,
bacteria (AOB) AmoA-2r (Rotthauwe et al., 1997) 57 °C 45s, 72 °C 45s, 78 °C 20s 53 °C 30s, 72 °C 1 min; 72 °C 10 min
Ammonia-oxidizing Arch-amoAf (Francis et al., 2005) 95 °C 5 min; 45 cycles: 95 °C 30s, 94 °C 5 min; 35 cycles: 94 °C 30s,
archaea (AOA) Arch-amoAr (Francis et al., 2005) 53 °C 45s, 72 °C 45s, 78 °C 20s 53 °C 45s, 72 °C 10 min; 72 °C 1 min
85
4.2.4 Physico-soil chemical analyses
Soil pH values were measured in water suspensions at a solid-to-liquid ratio of 1-to-2.5. Hot
water extractable C (HWEC) and N (HWEN) analyses were determined according to Schulz
(2004) as well as Schulz and Körschens (1998). C content of hot water extracts was analyzed
using potassium dichromate in sulfuric acid milieu (Multi N/C analyzer, Analytik Jena, Jena,
Germany), while HWEN was analyzed by a modified Kjeldahl digestion. Total C (TC) and
total N (Nt) of soils were quantified by dry combustion (Flash EA 1112 Elemental Analyzer,
Thermo Fisher Scientific). Ammonium (NH4+) and nitrate (NO3
-) were calorimetrically
measured with a spectrophotometer on field fresh soils (ICRAF, 1999). Soil subsamples of 20
g each were taken for moisture content (MC) determination by weight loss after drying (105
°C, 24 hours).
4.2.5 Statistical analysis
Analyses of variance (ANOVA) were conducted using Statistical Analysis Software program
SAS (SAS Institute, 2015). For the abundance data of the four studied genes, a generalized
linear mixed model with a negative binomial distribution error and a log link function was
used. The model was fitted by restricted log pseudo-likelihood with expansion about the
solution for the random effects in the SAS GLIMMIX procedure. For the geochemical data,
linear mixed models were fitted in the SAS MIXED procedure. The data were checked for
normality and homoscedasticity on model residuals using quantile-quantile (Q-Q) plots,
histograms and studentized residual plots. Different transformations were tried and selected
accordingly in an attempt to satisfy the assumption underlying the ANOVA model (Piepho,
2009). A full factorial model was specified with the effects of main factors “Soil type” (Humic
Nitisol (Embu), Ferric Alisol (Machanga)), “Organic input” (TD, CC, ZM, CON), “Growth
stage” (EC30, EC60), and their interactions as fixed effects on the response variables (i.e.,
abundance of the four target genes and geochemical properties). We accounted for the repeated
measurements in the same plots by fitting an error with a first order autoregressive covariance
structure to the data. Statistical significance of all effects was assessed at a significance level
of P < 0.05. Blocks were considered as fixed or random effect and model selected accordingly
using the Akaike Information Criterion (AIC). Treatment means/medians were separated using
the PDIFF option of the LSMEANS as well as the letters (letter display) from the SAS
generalized linear mixed models procedure. Descriptive mean values with standard deviations
for abundance of all the studied genes are presented as supplementary information (S4.6.1).
Pearson linear correlation analyses were conducted in the SAS COR procedure to relate the
86
abundance (dependent variables) of target genes to soil chemical properties (independent
variables).
Main factors and their interaction effects on TRFLP data sets generated for each ammonia-
oxidizing gene (i.e., AOB, AOA) were tested using permutation multivariate analysis of
variance (PERMANOVA) (Anderson, 2001). Treatment effects were further assayed based on
Bray-Curtis similarity coefficients (Clarke, 1993). A similarity matrix was generated for all
possible pairs of samples for each target gene (Rasche et al., 2014, 2011; Rees et al., 2005).
The similarity matrix was used for analysis of similarity (ANOSIM) to test the hypothesis that
composition of studied microbial communities was altered by the three factors. ANOSIM is
based on rank similarities between the sample matrix and produces a test statistic called ‘R’
(Rasche et al., 2014, 2011; Rees et al., 2005). A ‘global’ R was first calculated in ANOSIM,
which evaluated the overall effect of a factor in the data set. This step was followed by a
pairwise comparison, whereby the magnitude of R indicated the degree of separation between
two tested communities. An R score of 1 indicated a complete separation, 0 indicated no
separation, while a negative R score suggests that the greatest differences are within, instead of
between groups (Clarke and Gorley, 2001; Rasche et al., 2011; Rees et al., 2005). For graphical
visualization of the compositions of ammonia-oxidizing communities as shaped by the studied
factors, canonical analysis of principal coordinates (CAP) was performed on resemblance
matrix data generated based on Bray-Curtis similarity coefficients (Anderson and Ter Braak,
2003; Anderson and Robinson, 2003; Clarke, 1993). In addition, regression analyses were
performed based on Shannon-Weaver indices (H´, Shannon and Weaver, 1949) of the TRFLP
data to related the community composition (predicted variables) to soil chemical properties
(predictor variables) using distance-based linear models (DSTLM) (Boj et al., 2011; Legendre
and Anderson, 1999; McArdle and Anderson, 2001). DISTLM is a routine for analyzing the
relationship between a multivariate data, based on resemblance matrix, and one or more
predictor variables (Legendre and Anderson, 1999; McArdle and Anderson, 2001). It partitions
variation in a data set (i.e., community composition of target genes, soil chemical data)
according to regression models. PERMANOVA, ANOSIM, CAP and DISTLM analyses were
conducted using Primer6 for Windows (version 6.1.13, Primer-E Ltd., Plymouth, UK) with
(PERMANOVA+ version 1.0.6) add-on for Primer 6 software (Anderson, 2001).
87
4.3 Results
4.3.1 Dynamics of abundance of microbial communities
A full factorial analysis of variance (ANOVA) summary of the influence of all factors and their
interactions as fixed factors on the abundance of the studied genes is presented in Table 4.2.
Soil type exhibited the strongest effect and was significant for all microbial parameters tested
and exhibited in most cases a strong interaction with crop growth stage.
Total bacterial 16S rRNA gene copy numbers were generally higher in the clayey (1.97 x 109
copies g-1 dry soil) than sandy soil (1.11 x 109 copies g-1 dry soil) (P < 0.05) (Table 4.3). In
both soils, bacterial 16S rRNA gene copy numbers increased at flowering stage (EC60) (P <
0.05) (Table 4.3). Total archaeal 16S rRNA gene copy numbers were also higher in the clayey
soil (1.97 x 1010 copies g-1 dry soil) than in the sandy soil (2.54 x 108 copies g-1 dry soil) (P <
0.001) (Table 4.3). Archaeal abundance, however, showed a significant interaction with growth
stage being lower at EC60 than EC30 in the clayey soil, while a contrasting effect was observed
for the sandy soil (P < 0.05) (Table 4.3).
In contrast to total bacterial abundance, the sandy soil revealed a higher abundance of AOB
(2.48 x 108 copies g-1 dry soil) compared to the clayey soil (1.94 x 108 copies g-1 dry soil) (P <
0.05) (Table 4.3). In the clayey soil, AOB abundance was lower in all treatments at EC60 than
EC30 (P < 0.001), while it increased with advanced maize growth stage in the sandy soil (P <
0.05) (Table 4.3). Abundance of AOA was higher in the clayey soil (6.16 x107 copies g-1 dry
soil) compared to the sandy soil (1.34 x 107 copies g-1 dry soil) (P < 0.05) (Table 4.3). In the
clayey soil, AOA reduced at EC60 in all treatments compared to EC30 (P < 0.05), while no
significant differences in abundance of AOA was determined between crop growth stages in
the sandy soil (P > 0.05).
With respect to organic inputs, only total bacterial 16S rRNA gene and AOB abundance were
significantly affected by organic input quality. TD showed higher bacterial 16S rRNA gene
abundance than CC (P < 0.05) (Fig 4.1A).
AOB abundance further revealed a significant interaction between soil type, growth stages and
quality of organic inputs (P < 0.05). Respective analysis in the sandy soil only revealed that
TD and ZM had higher AOB amoA abundance than CC (P < 0.05) (Fig 4.2).
Both archaeal 16S rRNA gene and AOA amoA abundance did not respond to organic input
quality independent of soil (P > 0.05) (Fig 4.1B and 4.1C).
88
Figure 4.1: Gene copy numbers of total communities and ammonia-oxidizing archaea in
different treatments (mean values n = 12). (A) total bacteria (Bacterial 16S rRNA gene), (B)
total archaea (Archaeal 16S rRNA gene), (C) archaeal amoA (AOA) gene after application of
different biochemical quality organic inputs (4 Mg C ha-1 year-1). Different letters (a-c), above
the bars indicate treatments with significant differences (P < 0.05). Treatments: CON =
Control, CC = Calliandra calothyrsus, TD = Tithonia diversifolia, ZM = Zea mays.
89
Figure 4.2: Gene copy numbers of ammonia-oxidizing bacteria (AOB) in clayey and sandy
soils in different treatments after application of different biochemical quality organic inputs (4
Mg C ha-1 year-1) (mean values n = 6). Different letters (a-c), above the bars indicate treatments
with significant differences (P < 0.05). Treatments: CON = Control, CC = Calliandra
calothyrsus, TD = Tithonia diversifolia, ZM = Zea mays.
90
Table 4.2: Statistical evaluation of gene abundance, community composition and soil chemical properties by full
factorial ANOVA and PERMANOVA.
Effects/source of variation
Microbial groups and soil
properties
Units S EC OI S x EC S x OI EC x OI S x EC x OI
qPCR
Bacterial 16S rRNA gene (copies g-1 dry soil) *** *** ** n.s n.s n.s n.s
Archaeal 16S rRNA gene (copies g-1 dry soil) *** *** n.s *** n.s n.s n.s
Bacterial amoA gene (AOB) (copies g-1 dry soil) * *** ** *** * n.s *
Archaeal amoA gene (AOA) (copies g-1 dry soil) *** n.s n.s * n.s n.s n.s
TRFLP
Bacterial amoA gene (AOB) *** n.s * n.s * n.s n.s
Archaeal amoA gene (AOA) * n.s * n.s n.s n.s n.s
Soil properties
Soil pH n.s *** *** *** * n.s n.s
NH4+ (mg kg-1) * n.s n.s * n.s n.s n.s
NO3- (mg kg-1) n.s n.s n.s * n.s n.s n.s
TC (%) *** n.s n.s n.s n.s n.s n.s
Nt (%) *** ** * *** * n.s *
HWEC (mg kg-1) *** n.s ** * n.s * n.s
HWEN (mg kg-1) *** n.s *** *** ** n.s *
MC (%) *** *** n.s *** n.s n.s n.s
Abbreviations: S = Soil type, EC = Crop growth stage OI = Organic inputs, NH4+ = Ammonium, NO3
- = Nitrate, TC = Total carbon,
Nt = Total nitrogen, HWEC = Hot water extractable carbon, HWEN = Hot water extractable nitrogen, MC = Soil moisture content.
Significance levels: n.s: P > 0.05; *P < 0.05; **P < 0.01; ***P < 0.001
91
Table 4.3: Abundance of the four assayed microbial communities as influenced by soil type at different growth stages
of Zea mays during the study period.
EC30 = Young growth stage of Zea mays; EC60 = Flowering stage of Zea mays.
Values are means ± standard error for soil type effect (n = 24) (i.e., means are averages of abundance values of all treatments
for both crop growth stages) and mean values of growth stage effect (n = 12) are averages of all treatments for respective crop
growth stages according to soil type. Different letters (a-d) against the values indicate groups with significant differences within
each factor (P < 0.05).
Effects Total bacteria
(16S rRNA gene)
Total archaea
(16S rRNA gene)
Bacterial amoA gene
(AOB)
Archaeal amoA gene
(AOA)
Clayey soil 1.97 ± 0.17 x 109a 1.97 ± 0.29 x 1010a 1.94 ± 0.13 x 108b 6.16 ± 1.02 x 107a
Sandy soil 1.11 ± 0.09 x109b 2.54 ± 0.37 x 108b 2.48 ± 0.22 x 108a 1.34 ± 0.22 x 107b
EC30clayey 1.67 ± 0.16 x 109b 2.21 ± 0.34 x 1010a 3.19 ± 0.22 x 108a 9.74 ± 2.52 x 107a
EC60clayey 2.33 ± 0.23 x 109a 1.75 ± 0.27 x 1010b 1.17 ± 0.08 x 108c 3.89 ± 1.01 x 107b
EC30sandy 9.41 ± 0.91 x 108c 1.72 ± 0.26 x 108d 2.06 ± 0.14 x 108b 9.60 ± 2.49 x 106c
EC60sandy 1.30 ± 0.13 x 109b 3.75 ± 0.57 x 108c 3.00 ± 0.21 x 108a 1.87 ± 0.49 x 107bc
92
4.3.2 Dynamics of microbial community composition
PERMANOVA revealed individual effects of soil type and organic input quality on AOA and
AOB community composition (P < 0.05, Table 4.2). For AOB, a significant interaction
between soil type and organic input quality was determined (P < 0.05, Table 4.2). These effects
were strengthened by ANOSIM which showed a significant effect of soil type on AOB (Global
R = 0.505) and AOA (Global R = 0.373) community composition (P < 0.01) (Table 4.4).
Moreover, significant alterations of AOB community composition were observed between the
two crop growth stages within each soil type (i.e., clayey soil: Global R = 0.239 (P < 0.01),
sandy soil: Global R = 0.1 (P < 0.05)). No significant changes of AOA community composition
were found between the crop growth stages within each soil type (P > 0.05) (Table 4.4).
Organic input quality induced a significant influence on AOB composition (i.e., Global R =
0.14) for both soils (P < 0.05), while AOA composition was only altered by organic input
quality in the sandy soil (Global R = 0.291) (P < 0.01) (Table 4.4).
To assess the explicit effect of organic input quality on the composition of AOB and AOA
communities at specific growth stages, ANOSIM was performed individually for each soil type
(Table 4.5). In the clayey soil, AOB revealed a Global R = 0.244 (P < 0.05) at EC30 and a
Global R = 0.639 (P < 0.01) at EC60. In the sandy soil, a Global R = 0.225 (P < 0.05) was
revealed at EC30, while organic input quality did not reveal a significant Global R for AOB at
EC60 (P > 0.05). AOA community composition was only altered by biochemical quality of
inputs in the sandy soil at EC60 (Global R = 259; P < 0.05). Pairwise comparisons for AOB
composition in the clayey soil showed an R = 0.333 between CC versus ZM at EC30. At EC60,
an R = 0.704 was revealed between TD and ZM, while CC versus ZM showed an R = 0.815.
In the sandy soil, AOB revealed an R = 0.296 between TD versus CC (EC30) and an R = 0.222
between CC and ZM (EC30 and EC60) (Table 4.5). AOA community composition distinctions
in the sandy soil revealed an R = 0.333 and an R = 0.444 between TD versus CC at EC30 and
EC60, respectively, while CC versus ZM revealed an R = 0.481 at EC60. No effects of organic
input quality on AOA composition were found in the clayey soil (P > 0.05).
93
Table 4.4: Global R values for the three main factors “Site,” “Growth stage” and “Input type”
obtained from the analysis of similarity (ANOSIM) of TRFLP fingerprints (n = 48).
Factors Bacterial amoA gene (AOB) Archaeal amoA gene (AOA)
Site type 0.505** 0.373**
Growth stages (EC)
Clayey soil 0.239** n.s
Sandy soil 0.1* n.s
Organic inputs (OI)
Clayey soil 0.140* n.s
Sandy soil 0.141* 0.291*
Significance levels: n.s: P > 0.05; *P < 0.05; **P < 0.01
Table 4.5: Analysis of similarity (ANOSIM) to determine the influence of biochemical
quality of organic inputs on composition of total and ammonia-oxidizing prokaryotes in the
clayey and sandy soils at young growth stage (EC30) and flowering stage (EC60) of Zea
mays (n = 48).
Bacterial amoA gene (AOB) Archaeal amoA gene (AOA)
Clayey soil Sandy soil Clayey soil Sandy soil
EC30 EC60 EC30 EC60 EC30 EC60 EC30 EC60
Global
R
across
treatments
0.244*
0.639**
0.225*
n.s
n.s
n.s
n.s
0.259*
Pairwise
test groups
R statistic
CON vs. TD 0.259 0.185 0.407 0.148 0.333 -0.167 0.259 0.370
CON vs. CC -0.148 1.000 0.370 -0.037 0.148 0.056 0.222 0.444
CON vs. ZM 0.630 1.000 0.407 0.370 0.519 -0.056 0.019 -0.185
TD vs. CC -0.074 0.185 0.296 0.148 -0.185 0.019 0.333 0.444
TD vs. ZM 0.185 0.704 -0.148 0.074 0.074 -0.093 -0.296 0.037
CC vs. ZM 0.333 0.815 0.222 0.222 0.000 -0.111 0.148 0.481
Treatment codes: CON = Control, CC = Calliandra calothyrsus, TD = Tithonia diversifolia,
ZM = Zea mays. Significance levels: n.s: P > 0.05; *P < 0.05; **P < 0.01.
94
Canonical analysis of principal coordinates (CAP) was used to visualize the influence of the
studied factors (i.e., soil type, crop growth stage, biochemical quality of organic inputs) on
AOA and AOB community composition (Figs. 4.3 and 4.4). CAP ordination revealed distinct
effect of soil type and quality of organic inputs but not crop growth stages on composition of
AOB and AOA. For instance, ZM separated from CC and TD for AOB composition in the
clayey soil (Fig. 4.4C). Further, clear separations between TD versus CC and ZM versus CC
were evident for AOA composition in the sandy soil (Fig. 4.4D).
95
Figure 4.3: Canonical analysis of principal coordinates (CAP) for visual presentation of
patterns of composition of ammonia-oxidizing communities as shaped by soil texture [(A) and
(B)] as well as crop growth stages [(C) and (D)]. Ammonia-oxidizing communities: (A), (C) =
ammonia-oxidizing bacteria (AOB); (B), (D) = ammonia-oxidizing archaea (AOA). Crop
growth stages: EC30 = young growth stage, EC60= Flowering stage.
(A)
(C) (D)
(B)
96
Figure 4.4: Canonical analysis of principal coordinates (CAP) for visual presentation of
patterns of composition of ammonia-oxidizing communities as shaped by the contrasting
biochemical organic inputs (4 Mg C ha-1 year-1). (A) = ammonia-oxidizing bacteria (AOB) and
(B) = ammonia-oxidizing archaea (AOA). Treatments codes: E = Embu (clayey soil), M =
Machanga (sandy soil), CON = Control, TD = Tithonia diversifolia, CC = Calliandra
calothyrsus, ZM = Zea mays.
4.3.3 Effects of biochemical quality of inputs on soil chemical properties
Overall, soil pH values were not different between the clayey (pH = 5.13) and sandy (pH =
5.05) soil (P > 0.05) (Table 4.6). In more detail, ZM in the clayey soil had a higher pH than
CON and CC at EC60 (P < 0.05), but there was no organic input quality effect on soil pH at
EC30 (P > 0.05). In the sandy soil, TD showed higher pH than CON, CC and ZM at both crop
growth stages (P < 0.01). The latter also showed a higher pH than CC during both growth stages
(P < 0.05). The clayey soil showed higher NH4+ concentrations than the sandy soil (P < 0.05).
However, NH4+ concentration was neither influenced by crop growth stages nor by biochemical
quality of organic inputs within each soil type (P > 0.05). NO3- was not influenced by soil type
as well as biochemical quality of organic inputs (P > 0.05). Clayey soil had higher TC values
than the sandy soil (P < 0.001). On the other hand, crop growth stages and the biochemical
quality of organic inputs did not influence TC within each soil type (P > 0.05). Nt values were
higher in the clayey than the sandy soil (P < 0.001). In the clayey soil, Nt increased at EC60 (P
< 0.001). All plots which received organic input increased Nt compared to CON (P < 0.05), but
(A) (B)
97
no difference between biochemical contrasting organic inputs was measured (P > 0.05). In the
sandy soil, neither the crop growth stages nor organic input quality influenced Nt (P > 0.05).
The clayey soil revealed higher HWEC values than the sandy soil (P < 0.001). In the clayey
soil, TD had higher HWEC than ZM and CC at EC30 and EC60, respectively (P < 0.05). At
EC60, ZM showed higher HWEC than CC (P < 0.05). In the sandy soil, TD had higher HWEC
than CC and ZM at EC30 (P < 0.05). The clayey soil had higher HWEN than the sandy soil (P
< 0.001). In the clayey soil, HWEN was low at EC60 compared to EC30 (P < 0.05). ZM had
high HWEN compared to CC and CON at EC60 (P < 0.05). Biochemical quality of organic
inputs did not influence HWEN at EC30 (P > 0.05). On the other hand, an increase of HWEN
at EC60 was measured in the sandy soil (P < 0.001). TD revealed high HWEN compared to
CON, CC and ZM (P < 0.05). Soil moisture content (MC) was high in the clayey soil compared
to the sandy soil (P < 0.001) which corresponded with the precipitation patterns at the two
study sites. Accordingly, MC was high at EC30 compared to EC60 within each site (P < 0.001).
Overall, biochemical quality of inputs did not influence MC in both soil types (P > 0.05).
98
Table 4.6: Chemical properties of Embu (clayey) and Machanga (sandy) soils amended with biochemically contrasting
organic inputs (4 Mg C ha-1). Values are means for soils sampled at young (EC30) and flowering (EC60) stages of maize
crop after incorporation of organic inputs.
Soil properties
Factors Treatments Soil pH NH4+
[mg kg-1]
NO3-
[mg kg-1]
TC
[%]
Nt
[%]
HWEC
[mg kg-1]
HWEN
[mg kg-1]
MC
[%]
EC30
Clayey soil CON 5.03cd 2.8a 1.9c 2.27bc 0.18b 432.de 13.8d 38.1a
CC 5.09abcd 2.8a 3.9bc 2.67abc 0.21a 543abc 26.5ab 40.3a
ZM 5.40a 3.6a 2.1c 2.70abc 0.21a 500bcd 26.7ab 40.8a
TD 5.39ab 3.8a 4.5bc 2.73ab 0.21a 579a 28.2a 40.4a
Sandy soil CON 4.54D 2.9AB 5.9AB 0.32A 0.04A 220AB 3.9C 13.1AB
CC 4.14E 3.7A 7.2A 0.32A 0.04A 147B 4.1C 10.1B
ZM 4.64D 2.8AB 6.5AB 0.30A 0.04A 172B 4.9C 12.2AB
TD 5.18A 3.7A 7.9A 0.41A 0.05A 271A 11.9A 14.1A
SED ± 0.12 ± 0.50 ± 0.91 ± 0.007 ± 24.8 ± 1.08
EC60
Clayey soil CON 4.87d 4.0a 5.8ab 2.25c 0.18b 402e 13.3d 24.4b
CC 4.93d 4.0a 6.3ab 2.64abc 0.23a 478cd 19.9c 22.3b
ZM 5.08bcd 3.6a 6.0ab 2.74a 0.23a 549ab 24.6ab 25.1b
TD 5.28abc 3.7a 7.0a 2.71abc 0.23a 524abc 22.8bc 24.8b
Sandy soil CON 5.14C 2.5AB 2.7C 0.32A 0.04A 163B 3.6C 5.2C
CC 5.06C 2.7AB 4.0BC 0.30A 0.04A 275A 8.2B 2.6C
ZM 5.63B 2.3AB 4.3BC 0.31A 0.03A 248A 7.5B 3.4C
TD 6.06A 2.1B 5.5AB 0.42A 0.04A 263A 14.5A 3.6C
SED ± 0.12 ± 0.55 ± 0.91 ± 0.007 ± 25.6 ± 1.08
Soil type Clayey soil 5.13A 3.6A 4.7A 2.58A 0.21A 501A 21.6A 3.6A
Sandy soil 5.05A 2.9B 5.5A 0.34B 0.04B 220B 6.9B 2.9B
SED ± 0.07 ± 0.31 ± 0.50 ± 0.005 ± 12.7 ± 0.31
99
Table 4.6: Continued.
Soil
properties
Factors Treatments Soil pH NH4+
[mg kg-1]
NO3-
[mg kg-1]
TC
[%]
Nt
[%]
HWEC
[mg kg-1]
HWEN
[mg kg-1]
MC
[%]
Growth stages EC30clayey 5.23b 3.3ab 3.1b 2.59a 0.20b 513a 23.4a 39.9a
EC60clayey 5.04c 3.8a 6.3a 2.58a 0.22a 489a 19.9b 24.3b
EC30sandy 4.62d 3.2ab 6.8a 0.34b 0.04c 203b 5.8d 12.4c
EC60sandy 5.47a 2.5b 4.1b 0.34b 0.04c 234b 8.0c 3.7d
SED ± 0.08 ± 0.25 ± 0.46 ±0.004 ± 12.7 ± 0.54
Abbreviations: NH4+ = Ammonium, NO3
- = Nitrate, TC = Total carbon, Nt = Total nitrogen, HWEC = Hot water extractable carbon,
HWEN = Hot water extractable nitrogen, MC = Soil moisture content.
Treatment codes: CON = Control, CC = Calliandra calothyrsus, TD = Tithonia diversifolia, ZM = Zea mays.
Values are means for organic inputs quality effects (n = 3); soil type effect (n = 24); growth stage effect (n = 12).
SED = Standard error of the difference between two means for comparisons across organic inputs, growth stages and soil type.
Different letters (a-d/A-D), against the values indicate treatments with significant differences within factors accordingly (P < 0.05).
100
4.3.4 Linear correlations and regression analyses between soil chemical properties and
soil microbial communities
Total bacterial and archaeal 16S rRNA gene abundances were positively correlated with TC,
Nt, HWEC and HWEN in both soils (P < 0.01) (Table 4.7). Archaeal amoA (AOA) gene
abundance revealed positive correlations with TC, HWEC and HWEN in the clayey soil, while
positive correlations between bacterial amoA (AOB) gene abundance with TC, Nt and HWEN
were shown in the sandy soil (P < 0.05). Total archaeal, AOB and AOA gene abundances
revealed positive correlations with MC in clayey soil (P < 0.05). Negative correlations were
determined for AOB (P < 0.001) and AOA (P < 0.05) gene abundances with NO3- in the clayey
soil. Abundance of all studied genes in both soils, except AOA in sandy soil, revealed positive
correlations with soil pH (P < 0.05).
Distance-based linear models (DISTLM) analyses between ammonia-oxidizing AOB and
AOA community composition and soil chemical properties revealed significant relationships
in the clayey, but not in the sandy soil (Table 4.7). Accordingly, variation in AOB community
composition was explained by MC, Nt and NO3- (34 %, 24 % and 18 %, respectively; P < 0.05),
while the community composition variation of AOA was explained by soil pH (35 %), TC (34
%) and Nt (29 %) (P < 0.01).
101
Table 4.7: Linear correlations (r) and regression (r2) analyses between the soil chemical properties, abundance (qPCR) and composition
(TRFLP) of microbial communities (n = 48).
Bacterial
16S rRNA
gene
Archaeal
16S rRNA
gene
Archaeal
amoA
gene (AOA)
Archaeal
amoA
gene (AOA)
Bacterial
amoA
gene (AOB)
Bacterial amoA
gene (AOB)
Soil type Soil properties (qPCR) (r) (qPCR) (r) (qPCR) (r) (TRFLP) (r2) (qPCR) (r) (TRFLP) (r2)
Clayey Soil pH 0.52* 0.84*** 0.59** 0.35** 0.47* n.s
NH4+ n.s n.s n.s n.s n.s n.s
NO3- n.s n.s -0.44* n.s -0.68*** 0.18*
TC 0.63*** 0.83*** 0.55** 0.35** n.s n.s
Nt 0.73*** 0.63** n.s 0.29** n.s 0.24*
HWEC 0.65** 0.77*** 0.48* n.s n.s n.s
HWEN 0.56** 0.80*** 0.60** n.s n.s n.s
MC n.s 0.43* 0.71*** n.s 0.96*** 0.34**
Sandy Soil pH 0.55* 0.55* n.s n.s 0.75*** n.s
NH4+ n.s n.s n.s n.s n.s n.s
NO3- n.s n.s n.s n.s n.s n.s
TC 0.79*** 072*** n.s n.s 0.51* n.s
Nt 0.78*** 0.67*** n.s n.s 0.42* n.s
HWEC 0.41* 0.49* n.s n.s n.s n.s
HWEN 0.66*** 0.68*** n.s n.s 0.64*** n.s
MC n.s n.s n.s n.s n.s n.s
Abbreviations: NH4+ = Ammonium, NO3
- = Nitrate, TC = Total carbon, Nt = Total nitrogen, HWEC = Hot water extractable carbon,
HWEN = Hot water extractable nitrogen, MC = Soil moisture content.
Significance levels: n.s: P > 0.05; *P < 0.05; **P < 0.01; ***P < 0.001
102
4.4 Discussion
4.4.1 Response of microbial gene abundance and community composition to texture
The abundance of all the studied microbial communities was higher in the clayey than sandy soil
including AOB except for flowering stage. In addition, community compositions of AOB and
AOA were clearly distinct according to the soil texture. Influence of soil texture on dynamics of
soil microbial communities has widely been reported (Girvan et al., 2003; Morimoto et al., 2011;
Musyoki et al., 2015; Neumann et al., 2013; Rasche et al., 2014). The distinct influence of texture
on the dynamics of microbial communities in this study was attributed to differences in native soil
fertility status between Embu (clayey) and Machanga (sandy) soils as was revealed by soil
chemical properties. TC, Nt, HWEC and HWEN values were higher in the clayey than the sandy
soil which was also in line with initial soils characterization (Gentile et al., 2011a). Climatic
conditions which influence soil moisture content also contributed to higher microbial abundance
in the clayey soil. Accordingly, AOB and archaeal communities (total and AOA) abundances were
positively correlated with moisture content. However, dynamics of microbial communities were
not only confounded to soil texture but specific interaction effects between organic inputs and soil
texture on dynamics of AOB and AOA were also evident.
4.4.2 Response of AOB to interrelations of soil texture and organic input quality
Amendment of sandy soil with organic inputs had offset the presumed nutrient deficiency by
availing organic nutrient substrates at high levels favored by microbial groups including AOB (Di
et al., 2009; Musyoki et al., 2015; Wessén et al., 2011, 2010). Accordingly, the response of AOB
abundance to high availability of mineralized organic N was observed for N-rich TD during the
entire study period. Contrary, a reduced AOB abundance by intermediate quality inputs (CC) in
comparison to high (TD) and low (ZM) quality inputs was also evident. This was explained by
high polyphenol and lignin contents in CC binding organic C and N, thus limiting their availability
to microbial communities (Millar and Baggs, 2004; Palm et al., 2001b; Schmidt et al., 2013).
Promotion of AOB abundance in low and intermediate quality inputs was observed at flowering
stage (EC60) in the sandy soil substantiating their gradual decomposition (Partey et al., 2013; Palm
et al., 2001b). This effect was, however, more pronounced for the low quality inputs since its
related AOB abundance increased matching that of high quality TD. A similar trend was observed
103
for total bacterial and archaeal abundance in the sandy soil which was further attributed to the high
cellulose content in the low quality inputs (ZM) accelerating decomposition (Kunlanit et al., 2014;
Samahadthai et al., 2010). In the sandy soil, we noticed a promotion of bacterial abundance in the
control treatment (CON) at EC60 presumably induced by promoted excretion of rhizodeposits that
might have boosted AOB proliferation (Bürgmann et al., 2005). These assumptions were further
supported by positive correlations between AOB abundance with TC, Nt and HWEN indicating
that AOB utilized organic C and NH3 derived from mineralization of organic substrates (i.e.,
rhizodeposition, organic inputs). Our results clearly implied that when nutrients are limited in
sandy soils, AOB utilize mainly C and N substrates derived from freshly incorporated organic
inputs (Musyoki et al., 2015).
In accordance with our expectations, high abundance of AOB regardless of organic input quality
was revealed in the clayey soil, particularly at early growth stage (EC30). These findings were
attributed to the native resource rich characteristics of this particular clayey soil (Gentile et al.,
2011a) and corresponded to those observed by Musyoki et al. (2015) on the same soils. Moreover,
the dominance of AOB abundance under non-nutrient limiting conditions particularly in response
to high NH4+ levels was earlier reported (de Gannes et al., 2014; Jia and Conrad, 2009; Levičnik-
Höfferle et al., 2012). In addition, clayey soils protect freshly added organic inputs from immediate
access by microbial communities (Dieckow et al., 2009; Krull et al., 2001; Zinn et al., 2005). We
assumed that such protection mechanism along with the strong SOC background in the clayey soil
which provided sufficient substrates to microorganisms had most likely masked the effect of
quality of fresh organic inputs on AOB abundance.
In contrast to the abundance of AOB, protection of organic inputs by the clayey soil induced an
input quality dependent separation of AOB community composition between CC versus ZM and
TD versus ZM at EC60. This finding supported an argument by Krull et al. (2001) that protection
of organic C by a clayey texture only reduces the immediate decomposition rates, with organic
resources becoming available for decomposition over time. Such separation was therefore
attributed to the biochemical differences, particularly the C/N ratio, between ZM (high C/N) and
the low C/N inputs CC and TD (Gentile et al., 2011a; Palm et al., 2001b). This suggested that
when C was not a limiting factor, N was the driving force for AOB community composition
alterations. Wessén et al. (2010) also reported alterations of AOB composition due to C/N ratio
differences between straw and peat inputs in a supposedly non-nutrient limiting clay loam soil.
104
Hence, it could be deduced that the mechanism by clayey soil to protect organic inputs (availing
organic C) gave AOB a clear advantage of evolving to those communities being adapted to degrade
organic inputs of distinct biochemical quality.
However, no substantial separation of AOB community composition in response to organic input
quality was found in the sandy soil. This was attributed to organic C limitation as a substrate for
AOB due to low SOC sequestration levels compared to the clayey soil. In addition, we argued that
low organo-mineral associations in the sandy soil along with the limited surface area for adsorption
and less protection of AOB against predation were supposedly responsible for the inexistent AOB
composition alterations as explained (Dieckow et al., 2009; Kögel-Knabner et al., 2008). This
finding was further supported by Poll et al. (2003) and Sessitsch et al. (2001) who reported
substantial differences in total bacterial composition in clayey relative to sandy soils. It could
therefore be suggested that N is most likely the driving force for observed AOB community
alterations when C substrates are not limited.
4.4.3 Soil texture exert stronger effect on AOA than organic input quality
According to our hypothesis, higher abundance of AOA, regardless of biochemical quality of
organic inputs was determined in the clayey compared to the sandy soil. This was primarily
attributed to the characteristic high SOC retention of clayey soils providing sufficient C and N
substrates for AOA proliferation, which masked the effects of quality of organic inputs (Gentile et
al., 2011a; Musyoki et al., 2015; Zhang et al., 2010). These assumptions were corroborated by
positive relationships between AOA with TC and Nt in the clayey soil possibly implying that
archaea are adapted to decomposition of native and probably recalcitrant organic matter, in
addition to freshly added inputs in clayey soils.
We did not observe any interrelation between the clayey soil and organic input quality on the
community composition of AOA. This observation contradicted findings by Wessén et al. (2010)
in a clay loam soil of a long-term fertilization field experiment in Ultuna (Sweden). According to
Rasche and Cadisch (2013), faster turnover rates of SOC in tropical compared to temperate
conditions might have reduced organic matter accumulation to limits whereby, in comparison to
AOB, alterations of AOA community composition were not detectable. This implied that AOA
was most likely less sensitive to low SOC in the tropics compared to temperate regions.
On the other hand, we determined effects of organic input quality on the community composition
of AOA only in the sandy soil. This was particularly evident between TD versus CC as well as CC
105
versus ZM at EC60. These alterations were attributed to higher polyphenol contents in CC causing
the formation of chemical bonds (i.e., polyphenols-proteins bonds) which limited microbial access
to organic N and C as opposed to TD and ZM (Millar and Baggs, 2004; Palm et al., 2001b). The
findings suggested that polyphenol and lignin contents are the driving force for AOA composition
alterations in soil conditions where C and N substrates are simultaneously limiting.
4.5 Conclusions
We observed that organic inputs of contrasting biochemical quality induced distinct responses to
functional microbial groups of bacteria in specific soil types. We linked this to the background
SOC between the two soil types as SOC regulates C substrates to microbes. For example, AOB
abundance responded to soil amendments in the sandy soil when C was limiting. Specifically, high
(TD) (i.e., low C/N) and low (ZM) (i.e., high C/N) quality inputs increased AOB abundance in
comparison to the intermediate (CC) quality inputs suggesting that AOB primarily utilized both
available organic N and C from fresh inputs for proliferation. On the other hand, its composition
was mainly altered by N availability in the clayey soil when organic C was not limiting.
Accordingly, alterations of AOB in the plots which received high N residues such as TD and CC
from those which received low N such as ZM implied that N was the driving force to AOB
composition dynamics provided that C substrates were available.
Contrastingly, AOA composition was altered by inputs amendment in the sandy soil when C
substrate was limiting. Distinct separations of AOA assessed between plots that received TD and
CC as well as between CC and ZM, implied that polyphenol and lignin contents are the driving
force for AOA composition alterations in soil conditions where C and N substrates are
simultaneously limiting. Variations in responses of AOB and AOA to interrelations of soil texture
and organic inputs also support the acknowledged concept of niche differentiation between AOB
and AOA (Jia and Conrad, 2009; Levičnik-Höfferle et al., 2012; Verhamme et al., 2011) with AOB
being more adapted to high resource conditions than AOA which responded to recalcitrant
substrates. As this work was based on DNA analysis, RNA-based approach is recommended to
capture those communities that are actively involved in nitrification process.
106
4.6 Supplement
Supplement S4.6.1: Gene copy numbers of the four assayed genes in clayey and sandy soil types
in the different treatments (descriptive means ± standard deviation, n=3): (A) total bacteria
(bacterial 16S rRNA gene), (B) total archaea (archaeal 16S rRNA gene), (C) ammonia-oxidizing
bacteria (AOB, bacterial amoA gene), and (D) ammonia-oxidizing archaea (AOA, archaeal amoA
gene) after application of different biochemical quality organic inputs (4 Mg C ha-1 year-1). Crop
growth stages: EC30 = young growth stage, EC60= Flowering stage. Treatments: CON = control,
CC = Calliandra calothyrsus, TD = Tithonia diversifolia, ZM = Zea mays.
107
Chapter 5 General discussion
The main goal of the presented PhD work was to understand the impact of long and short-term use
of organic and inorganic inputs on the population dynamics of soil microbial communities
involved in nitrification process in two contrasting environments in the tropics. Knowledge on
dynamics of microbial communities is underpinned by their fundamental roles as regulators of
SOM turnover, provision of organic nutrients in inorganic forms that are accessible to plants as
well as being indicators of soil quality. The latter, although mainly ignored due to lack of tangible
and immediate benefits, is a critical tool in assessment of sustainable management of soil
resources. Consideration of contrasting soil types reflected the environmental differences existing
in the tropics.
Development of molecular tools, and more importantly the affordability of PCR-based approaches
like qPCR and TRFLP techniques laid the foundation to undertake this study. Although these
approaches were limited to use of 16S rRNA genes rather than the whole genome DNA (chapter
one), they made it possible to understand and draw important conclusions with regards to the
dynamics of soil microbial communities and their interactions with the environment. For example
using the qPCR (DNA-based functional potential abundance) and TRFLP (diversity), it was
possible to prove, and therefore contribute to science that biochemical quality of organic resources
combined with synthetic fertilizers and their interactions with abiotic factors like precipitation and
both chemical and physical properties of soils influence the populations of microbial communities
in distinctive ways. Such basic information is relevant to understand the function of the ecosystem
of concern and could form basis for further analyses using more expensive but whole community
analyses approaches. Integration of soil and input types with climate as well as soil chemical
properties are therefore worth considering when deliberating on viable means to solve the issue of
soil degradation if sustainable food production particularly in the developing countries has to be
achieved.
These factors were studied and presented in various chapters in this thesis with specific objectives
and obvious findings intended for publication. Thus, all the chapters contribute to a common
objective of understanding how in the long and short-term, the biochemical quality of organic
inputs and combination with synthetic fertilizer shape the dynamics of soil microbial communities.
108
The major findings of this thesis are contextualized and future work directions provided in the
following sections.
5.1 Is continuous application of organic resources to boost SOM a solution to soil degradation
in the tropics?
This question was resolved through comparison of the results of initial soil characterization and
those generated in year 2013. Table 5.1 therefore presents an overview of changes of total carbon
(TC), total nitrogen (Nt) and soil pH over 11 years since the establishment of the SOM trials in
clayey and sandy soil types in Kenya in 2002. Continuous cultivation without and with application
of organic residues resulted in degradation of soil resource in both soil types. In comparison to
start of the experiment, TC was reduced by 40 % in the absolute control plots and by 35 % in the
organic residues treated plots, whereas a 33 % reduction in Nt was determined for both absolute
control (CON) and organic inputs (OR) treated plots in Machanga site (sandy soil) (Table 5.1). At
Embu site (clayey soil), TC was reduced by 23 % and 8 %, whereas Nt reduced by 35 % and 21 %
in CON and OR treated plots respectively. The higher rate of C loss in the sandy soil was explained
by its low protection of freshly added organic matter from microbial activities to meet their C
substrate demand (Puttaso et al., 2011). In addition, increased mineralization rates most likely
occurred to provide nutrients for plants uptake since the soil is nutrient limited (Chivenge et al.,
2009; Gentile et al., 2011a; Mtambanengwe et al., 2006). On the other hand, the low rate of C loss
in the clayey soil was apparent due to its native high SOC background (Gentile et al., 2011a)
coupled with strong protection of recently added organic residues. In addition, C retention in
clayey compared to sandy soil was associated with formation of clay sized organo-mineral
associations promoted by addition of organic residues (Dieckow et al., 2009; Kögel-Knabner et
al., 2008; Jones and Singh, 2014). Similar observations were evident for Nt and soil pH, with soil
potential N being very limiting in Machanga site. In summary, TC has remained moderate at Embu
site but deteriorated from low to very low at Machanga site for the 11 years considered in this
study (Tekalign, 1991). Nt has significantly reduced from high to moderate at Embu site with a
worsening scenario at Machanga site which has reached a limiting state (Hazelton and Murphy,
2007; Tekalign, 1999). However, soil texture for Embu and Machanga soils has remained
unaffected after 11 years of continuous application of biochemically different quality organic
residues (Chapter four). Overall, soil degradation rate is soil type specific. Continuous application
109
of organic residues alone particularly in the tropics seems therefore, not a solution to soil
degradation in general. Nevertheless, repeated application of residues provides a substantial buffer
to soil degradation as shown by lower deterioration percentages of soil physico-chemical
properties in soils treated with organic residues compared to the absolute control. This therefore
justifies the integration of organic and inorganic soil inputs (Vanlauwe et al., 2010).
Table 5.1: Changes in soil physico-chemical properties after 11 years of repeated applications of
biochemically different quality organic residues (4 Mg C ha-1 year-1) in comparison to the start of
the SOM experiments in year 2002. Values are means of respective number of observations as
indicated per column.
Year Year 2013 Year 2013 [%] [%]
2002 (CON) (OR) reduction reduction
Site Soil properties (n =3) (n = 6) (n = 18) (CON) (OR)
Machanga TC [g kg-1] 5.3 3.2 3.4 40 35
Machanga Nt [g kg-1] 0.6 0.4 0.4 33 33
Machanga Soil pH 6.13 4.84 5.12 21 16
Embu TC [g kg-1] 29.3 22.6 27.0 23 8
Embu Nt [g kg-1] 2.8 1.8 2.2 35 21
Embu Soil pH [water] 5.81 4.95 5.20 14 10
Soil chemical parameters: TC = Total carbon, Nt = Total nitrogen. Treatment codes: CON =
absolute control; OR = Organic residues regardless of their quality since there were no residue
quality effects on TC and Nt values in year 2013 (Chapter four). Data for year 2002 was sourced
from Gentile et al. (2011a).
5.2 Implications of quality of organic resources and mineral N fertilizer on dynamics of
microbial communities in a clayey soil
A substantial understanding of the long and short-term effects of quality of organic resources and
mineral N fertilizer on the dynamics of soil microbial communities has been achieved by this study
(chapters two and three). Although a prolongation of the experimental period was recommended
to clearly capture the effects of interrelations of soil moisture content as dictated by precipitation
patterns with quality of organic inputs (chapter three), and also to achieve conclusive findings
particularly on the composition of total communities (chapter two), clear effects of quality of
110
organic resources on population dynamics of microbial communities were demonstrated. As
revealed and discussed in chapter two and three, the biochemical quality of organic resources
(CC, ZM, TD) regulated the abundance and composition of total and ammonia-oxidizing
communities (Muema et al., 2015). In the mid to long- term continuous application of organic
inputs, high quality inputs (TD) promoted the abundance of microbial communities, whereas
intermediate quality inputs (CC) decreased the abundance, while low quality inputs portrayed
specific effects on abundance to distinct microbial groups (chapter two, Fig. 2.2). These findings
were explained by the contrasting proportions of organic N, lignin as well as polyphenol contents
in the materials (Millar and Baggs, 2004; Palm et al., 2001b). Regulation of abundance and
composition of microbial communities by the biochemical quality of organic resources have been
reported also elsewhere (Chèneby et al., 2010; Hai et al., 2009; Kamolmanit et al., 2013; Rasche
et al., 2014; Wessén et al., 2010). Alterations of the composition of microbial communities by the
biochemical quality of organic resources occurred to a minor extent (chapter two, Fig. 2.4). This
justifies the recommendation to prolong the experiment for more conclusive answers. Similar
findings were observed in the short-term, (i.e., after fresh application of organic inputs) except that
AOA abundance remained unaffected (chapter three, Fig. 3.2). Sole use or combination of
mineral N fertilizer with organic inputs regardless of their quality revealed a decrease in abundance
of microbial communities except for the AOB abundance (chapter two, Fig. 2.2; chapter three,
Table 3.3). This decrease in abundance of microbial communities was attributed to the indirect
reduction of soil pH by the mineral N fertilizer suppressing microbial proliferation. It implied that
AOB are less sensitive to pH changes. Similar findings were also reported by Wessén et al. (2010).
Another possible explanation was the chaotropic nature of NH4+and NO3
- components of the
inorganic fertilizer reducing water activity in soils hence limiting microbial growth (Chapter
three). Chaotropicity refers to the ability of solutes to induce stress to microbial communities by
destabilization of macromolecular systems as a consequent of reduced water activity (de Lima
Alves et al., 2015; Hallsworth et al., 2003). However, since this study did not focus on water
activity measurements (de Lima Alves et al., 2015; Stevenson and Hallsworth, 2014), this potential
effect was proposed for consideration in future related studies. In summary, quality of SOM,
whether accumulated over mid to long-term or freshly added into clayey soil, influences the
abundance and composition of ammonia-oxidizing communities.
111
5.3 Implications of interrelations of soil texture and quality of organic residues on abundance
and composition of microbial communities
Consideration of two distinct soil types (clayey and sandy) in the tropics provided an
understanding of the interrelations between soil texture and quality of organic residues and their
impact on microbial communities (chapter four). The linkage between soil microorganisms, soil
particles and SOM, particularly the freshly incorporated organic residues, is soil aggregation, and
has consequences on soil productivity (Chivenge et al., 2009; Fonte et al., 2009; Gentile et al.,
2011a; Lucas, 2013). Soil microorganisms promote soil aggregation while decomposing the added
organic residues (Six et al., 2004; Tisdall, 1991). Studies conducted in the same experiment by
Gentile et al. (2011a) showed promotion of soil aggregation by addition of organic residues but
was not related to their quality in both soil textures. Consequently, it was proposed that the strong
soil structure associated with clayey soil would protect the freshly incorporated organic inputs
hindering their immediate accessibility by microbial communities thereby masking the effects of
input quality on microbial communities. On the other hand, the coarse structure of sandy soils
would provide less protection of organic inputs rendering them accessible to microbial
communities and hence result in fast decomposition. Accordingly, distinct responses of different
microbial groups to interrelations of soil texture and organic inputs quality were revealed. For
example, AOB abundance was influenced by quality of organic inputs in the sandy soil but
remained unaffected in the clayey soil (chapter four, Fig. 4.1C). An opposite scenario was true
for its composition (chapter four, Fig. 4.2C and 2D). Total bacterial abundance was influenced
by inputs quality in both soil types (chapter four, Fig. 4.2A). On the other hand, archaeal
communities (total and AOA) genes abundance were not affected by quality of inputs (chapter
four, Fig. 4.IB and 1D). However, a clear soil texture distinction was observed (chapter four,
Table 4.3). Moreover, AOA composition was altered by input quality in the sandy soil (chapter
four, Fig.4.2C and 2D). In addition, crop growth stages influenced microbial dynamics probably
due to interactions with the crop or soil moisture changes (Onwoga et al., 2010). This was more
pronounced in the clayey soil with reduction of communities at flowering stage associated with
the competition for nutrients and moisture with the crop. However, an increase of AOB abundance
was observed at flowering stage of maize in the sandy soil which was associated with a delayed
decomposition of low and intermediate quality residues as well as carbon substrates provision to
microbial communities through root exudations (Bürgmann et al., 2005). Overall, soil texture
112
residues interrelated effects were specific to microbial groups with AOB being more sensitive than
AOA. This supported the concept of niche differentiation between AOB and AOA which reduces
their competition for substrates (Jia and Conrad, 2009; Levičnik-Höfferle et al., 2012; Verhamme
et al., 2011).
5.4 Microbial dynamics versus crop performance as shaped by quality of residues and
mineral N fertilizer
From the findings in Chapter two, three and four, distinct effects of quality of organic residues
on the dynamics of microbial communities have been reported. High quality organic resources
promoted the abundance of microbial communities but the intermediate quality resources
decreased the abundance of communities. Low quality Zea mays (ZM) inputs on the other hand
demonstrated contrasting effects on microbial communities depending on microbial group and
crop growth stage. For example, in a clayey soil the abundance of AOB and total archaea was
decreased by ZM, while that of AOA and total bacteria was promoted (chapter two, Fig. 2.2). In
the sandy soil at young plants growth stage, the abundance of AOB and total bacteria was
decreased but promoted at flowering stage by ZM (chapter four, Fig. 4.1C and S4.6.1). In
addition, mineral N regardless of type of organic resources decreased the abundance of microbial
communities except that of AOB (chapter two, Fig. 2.2 and chapter three, Table 3.3).
Additionally, clear specific effects of quality of organic resources and mineral N on the
composition of AOB and AOA were reported (chapter two, Figs. 2.3, 2.4 and chapter four, Fig.
2), which emphasized the role of C/N ratio, polyphenol contents as well as mineral fertilizer in
regulation of dynamics of microbial communities.
On the other hand, in both clayey and sandy soils, high (TD) and intermediate (CC) quality organic
resources did not reveal significant treatment differences on maize grain yields. However, their
grain yields were significantly higher compared to low quality inputs (ZM) over ten seasons
(Chivenge et al., 2009). In addition, combination of low quality inputs with mineral N fertilizer
compared to sole use of inputs improved grain yields specifically in the nutrient limited sandy soil.
Similar findings on maize grain yields were observed in a sandy soil in Zimbabwe
(Mtambanengwe et al., 2006).
From these two scenarios, high quality inputs (TD) followed a similar principle of promotion of
abundance of microbial communities and increase of crop yields. This implied that their high and
113
easily accessible organic N content was sufficient to support microbial activities and plants uptake
simultaneously. Intermediate quality inputs on the other hand decreased microbial communities
but increased crop yields. The improvement of crop yields was attributed to high initial N in CC
(Chivenge et al., 2009; Vanlauwe et al., 2005) coupled with gradual release of mineralized N
throughout the growing season ensuring constant N supply which coincided with plants nutrients
demand boosting the yields. Nevertheless, its high polyphenol and lignin contents probably caused
the decline of microbial communities by favoring only those which could degrade these
recalcitrant materials which was supported by the microbial composition data (Chapter two and
four). Low quality inputs were characterized by contrasting effects on the abundance and
composition of microbial communities but a clear decrease of crop grain yields as a consequence
of their high C/N leading to N immobilization. This scenario agrees with suggestions that some
microbial groups like AOA have alternative nutrient sources (Blainey et al., 2011; Chen et al.,
2008; Stopnišek et al., 2010) unlike plants which heavily rely on readily available soil mineral
nutrients. Combination of organic inputs with mineral N fertilizer reduced the abundance of AOA
as well as total communities and altered the composition of AOB including total communities.
Although this study did not measure and link soil nitrification potential to community dynamics
of AOB and AOA communities, reduced abundance of AOA might not have necessarily resulted
to reduced nitrification. This was because AOB abundance remained unchanged and might
therefore have been responsible for nitrification. On the other hand, if reduction in AOA abundance
reduced nitrification rate, it would have been an added advantage to the crops as NH4+ would be
retained in the soil for plants uptake (Subbarao et al., 2013). This latter argument would therefore
support the general greater maize yields observed upon combination of organic resources and
mineral N fertilizer than sole application of individual resource (Chivenge et al., 2009). However,
negative interaction effects, i.e. yields as a result of combination of organic resources and mineral
N were not large enough to surpass the yield increase brought about by the sum of the separate
addition of the two resources, were observed (Chivenge et al., 2009). These were attributed to high
levels of N both from organic inputs and high rate of fertilizer masking the possible positive
interaction effects (Chivenge et al., 2009). However, at the sandy soil field trial, low quality inputs
combined with mineral N revealed positive interaction effects compared to their sole use due to
the fertilizer compensation effect on N limitation. Overall, it was deduced that high quality inputs
followed a similar principle in influencing the dynamics of microbial communities and crop
114
performance while intermediate and low quality inputs followed different principles, contrastingly
influencing the dynamics of microbial communities and crop performance.
5.5 Conclusions
From the research findings and implications of this PhD research firstly, quality of SOM whether
accumulated over mid to long-term or freshly added into clayey soil had effects on the abundance
and composition of ammonia-oxidizing communities. In relation to the size of microbial
communities, high quality organic resources promoted their abundance whereas the intermediate
quality residues led to their decrease. On the other hand, low quality residues either promoted or
decreased the abundance of microbial communities dependent on microbial group and the crop
growth stage. The contrasting quality attributes of organic residues induced separation of
composition of communities too. In this regard, microbial groups particularly the ammonia-
oxidizing communities differentiated to those communities that were sensitive to high N content,
those that could degrade recalcitrant resources from those that could tolerate N limiting conditions.
Sole use of mineral N fertilizer or its combination with organic residues decreased the abundance
of microbial communities apart from the AOB indicating its lower sensitivity to pH changes
created by mineral fertilizer. Secondly, the effects of interrelations of soil texture and quality of
organic resources were specific to microbial groups with AOB being more sensitive than AOA. In
this respect, sandy soils revealed effects of organic inputs quality on the abundance of AOB and
altered the community composition of AOA. On the other hand, clayey soil induced a considerable
alteration of the community composition of AOB in response to biochemical quality of organic
inputs. These findings supported the concept of niche differentiation between AOB and AOA
which reduces their competition for substrates (Jia and Conrad, 2009; Levičnik-Höfferle et al.,
2012; Verhamme et al., 2011).
The findings of dynamics of microbial communities were compared to those of crop yields with
regard to use of organic residues of different biochemical qualities in the same soils. It was deduced
that high quality inputs followed a similar principle in modifying the abundance of microbial
communities and crop yields. Intermediate and low quality inputs followed different principles,
contrastingly influencing the abundance of microbial communities and crop performance. Finally,
it was also found out that soil degradation was specific to soil type and that continuous application
115
of organic residues particularly in the tropics alone (at least at the tested rate) is not a general
solution to soil degradation. Nevertheless, repeated application of residues provided a substantial
buffer to soil degradation which should be embraced for sustainable management of soil resource.
5.6 Future directions
Although this study provides substantial insights on influences of agricultural practices on
microbial ecology linked to soil N cycle in SSA, it was mainly limited to the nitrification process.
In order to have a more complete understanding of N dynamics in the soil ecosystem, focus on
other processes which include nitrogen fixation, mineralization, ammonification as well as
denitrification within the N-cycle is highly recommended (Hai et al., 2009; Zhang et al., 2013).
DNA-based analyses were essential to understand the influence of different quality of organic
resources and mineral N fertilizer on dynamics of microbial communities. However, use of RNA-
based analyses are recommended to show those particular communities that are actively involved
in specific soil processes like nitrification (España et al., 2011).
Further understanding of N dynamics at a process or activity level can be achieved by carrying out
enzymatic analysis (Kamolmanit et al., 2013). Linkages of community diversity and abundance
data with nitrification and other potential functions would improve the understanding of the soil
ecosystem functions (Wessén et al., 2010).
The role of microbial communities on global climate change is not completely understood.
Therefore, the above proposed linkages of specific microbial community data with potential
functions in consideration of abiotic factors like precipitation, temperatures among others could
improve the understanding of how microorganisms affect climate change and possible mitigation
measures. In addition, data on microbial abundance and composition could be incorporated in
climate models to reduce uncertainties and improve estimation and predictions of future effects of
climate change (Allison et al., 2013; Singh et al., 2010; Todd-Brown et al., 2012).
In order to specifically understand interrelations of soil texture and quality of organic inputs on
soil microbial dynamics, consideration of controlled environments in future is recommended to
avoid the influence of environmental factors like precipitation which cannot be eliminated in the
field experiments.
116
Only one rate of application of mineral N fertilizer (120 kg N ha-1 season-1) was considered for
combination with different organic residues. This led to a decrease in abundance of microbial
communities in addition to negative interactions on maize performance specifically with high and
intermediate quality residues (Muema et al., 2015; Chivenge et al., 2009). Future studies should
consider testing different, but lower application rates of mineral fertilizer N. This would prevent
negative potential effects of excess N especially with high and intermediate quality inputs, like
contamination of the environment such as water bodies which is ecology unfriendly (Galloway et
al., 2008; Peter et al., 1997; Subbarao et al., 2013).
Scaling up of findings is critical for the farmers. Therefore, future trials should be set up in farmers’
fields so that they can have a sense of ownership of the research findings and interpretation
generated. Such an approach would convince farmers to prioritize use of organic resources as soil
inputs and perhaps be encouraged to grow some of the shrubs by their farm sides as hedge crops.
ISFM technology particularly the importance of organic compared to inorganic fertilizers has not
yet been well understood or appreciated by the policy makers. Therefore, organic inputs are not
well integrated in decision making systems at a national level. It would be important to emphasize
on their importance at the grass route levels of governance.
117
Summary
Tropical agro-ecosystems are limited in nutrient resources as a consequence of i) being composed
of highly weathered soils, ii) low native soil organic matter (SOM) content due to conversion of
natural forests to arable lands and iii) continuous cropping without replenishing soil nutrients.
Recovery of SOM by use of organic residues is faced with other competing uses like animal fodder.
Moreover, existing SOM is further reduced by increased turnover rates due to favorable climatic
conditions in the tropics. Incorporation of residues is therefore a justified means to restore SOM
and to provide crop nutrients through microbial mediated activities like nitrification. Nitrification
is a central step of the nitrogen (N) cycle, whereby ammonia is converted into nitrite and then to
nitrate by bacteria and archaea through production of the amoA gene encoding the α-subunit of the
enzyme ammonia monooxygenase. In order to better understand the impact of organic residues of
contrasting biochemical quality (i.e., high quality Tithonia diversifolia (TD; C/N ratio: 13, lignin:
8.9 %, polyphenols: 1.7 %), intermediate quality Calliandra calothyrsus (CC; 13, 13, 9.4) and low
quality Zea mays (ZM; 59, 5.4, 1.2)) on nutrient provision, effects of residue quality on dynamics
of relevant decomposer microbial communities were studied. In addition, mineral N fertilizer was
used to compensate for mineral N limitations especially in case of low and intermediate quality
residues. Since N is one of the most limiting crop nutrients in the tropics, this study therefore
focused on ammonia-oxidizing prokaryotes, using DNA-based quantitative PCR (qPCR) and
terminal restriction fragment length polymorphism (TRFLP) techniques. In addition, soil
physicochemical properties were measured and linked to the dynamics of microbial communities.
The study hypothesized that soil type due to differences in structure and nutrient background, as
well as seasonality, which influences soil moisture, would shape the response of the studied
communities to biochemical quality of residues.
In the first part of this research project (chapter two), the long-term response of abundance and
composition of total and ammonia-oxidizing prokaryotes to biochemically contrasting residues
and their combination with mineral N fertilizer in a clayey soil was determined. From the results,
the abundance of all studied genes was promoted by high quality inputs of TD due to their high
amounts of readily available N. However, they were reduced by intermediate quality inputs (CC)
which was linked to its high polyphenol and lignin contents. This fact induced N limitation via the
formation of polyphenol-protein bonds protecting organic N from microbial decomposition. TD
and CC also showed a separation with regards to AOA composition. Low quality maize inputs
118
(ZM) due to organic N limitation compared to TD decreased the abundance of AOB and total
archaeal genes and revealed a separation of AOA and AOB compositions. However, the promotion
of AOA abundance by ZM was attributed to its high affinity for ammonia under low N conditions.
Residues, regardless of quality, upon combination with mineral N decreased the abundance of
communities except AOB and altered their composition apart from AOA. This was attributed to
the indirect reduction of soil pH by the fertilizer with AOB abundance being less sensitive to pH
changes compared to AOA. The findings suggested residue type and mineral N dependent effects
on dynamics of AOB and AOA.
In the second part (chapter three), building on the results of first study in the clayey soil,
seasonality effects particularly the precipitation were considered. To achieve this, data from two
consecutive long rains seasons in years 2012 and 2013 was integrated. Two distinct growth stages
(young and flowering) of Zea mays were also considered. The results revealed temporal responses
of abundance of AOB but not AOA genes to biochemical quality of residues. Generally, a similar
trend of organic input effect on AOB abundance as in chapter two was revealed, except that ZM
increased AOB abundance at flowering stage of maize. This was a confirmation of delayed
decomposition of ZM throughout the season as a result of N limitation. Use of N fertilizer or its
combination with organic inputs revealed similar results as explained in chapter two. This
emphasized the lower sensitivity of AOB abundance to pH changes compared to AOA. In addition,
precipitation, regardless of residue quality, distinctly influenced all studied genes apart from AOA.
Accordingly, AOB and total archaeal abundance increased while that of total bacteria reduced in
year 2013 which was drier compared to 2012. It was thus suggested that optimal water filled pore
space, which favored proliferation of AOB, was achieved in year 2013. Total archaea were adapted
to reduced precipitation while higher precipitation in year 2012 favored the proliferation of total
bacteria. Overall, the results suggested that the effects of interrelations of residue quality and
seasonality, which also regulate soil moisture, contributed to niche differentiation between AOB
and AOA.
In the third component (chapter four), a sandy soil texture was considered in addition to clayey
soil, which was the focus of the previous two studies. The two contrasting soils showed specific
interrelations with SOM with regards to their soil structure as supported by their differences in
background soil organic carbon (SOC). It was thus hypothesized that such differences will lead to
119
distinct effects on dynamics of microbial communities. In clayey soils, apart from high SOC, clay
particles form organo-mineral associations, which provide a large surface area for proliferation
and protection of microorganisms from predation, favored a higher abundance of AOB and AOA
regardless of residue type in this study. On the other hand, sandy soils having a coarse structure
providing less protection of freshly added residues from microbial access. Interrelations of sandy
texture and quality of organic residues promoted AOB abundance and altered AOA composition.
Accordingly, TD (high organic N) and ZM (high organic C) compared to CC with high polyphenol
and lignin contents promoted the abundance of AOB and altered AOA composition. Interrelations
of the clayey soil with organic residues altered AOB composition. Accordingly, AOB was
separated between TD versus ZM and CC versus ZM. These findings implied that AOB was able
to access N from CC and that N was the driving force for AOB alterations when C was not limited
(clayey soil). On the other hand, AOA composition alteration was only possible when C and N
substrates were simultaneously limiting, as in the case of the studied sandy soil. Therefore, soil
texture and residues interrelations effects are microbial group specific with AOB being more
sensitive compared to AOA.
Overall, the results of this PhD research revealed specific responses of dynamics of AOB and AOA
to quality of organic residues and their combinations with mineral N fertilizer. They also revealed
effects of interrelations between quality of residues and soil texture as well as seasonality
particularly precipitation on dynamics of microbial communities. Future investigation of active
microbial communities with the use of RNA-based approaches need to be considered to further
improve our understanding of quality of SOM on soil nutrient dynamics.
120
Zusammenfassung
Tropische Agrarökosysteme haben limitierte Nährstoffressourcen als Folge von i) stark
verwitterten Böden, ii) einem niedrigem Gehalt an natürlicher organischer Bodensubstanz (OBS)
aufgrund der Konversion von natürlichen Wäldern zu landwirtschaftlicher Nutzfläche und iii)
kontinuierlichem Ackerbau ohne ergänzende Nährstoffzufuhr. Die Wiederherstellung der OBS
durch Einarbeitung von organischen Ernterückständen steht in Konkurrenz zu anderen Nutzungen
wie etwa Tierfutter. Aufgrund der günstigen klimatischen Bedingungen in den Tropen wird
darüber hinaus die bestehende OBS durch erhöhte Umsatzraten weiter reduziert. Daher ist für den
Wiederaufbau der OBS und von Pflanzennährstoffen, die durch mikrobielle Aktivitäten wie
Nitrifikation bereitgestellt werden, die Einarbeitung von Ernterückständen eine zwingende
Notwendigkeit. Nitrifizierung ist ein zentraler Schritt im N-Kreislauf, wobei das mineralisierte
Ammonium zuerst in Nitrit und danach in Nitrat von Bakterien und Archaeen durch das amoA
Gen, welches die α-Untereinheit des Enzyms Ammoniummonooxygenase (AMO) codiert,
umgewandelt wird. Um die Auswirkungen von Ernterückständen von kontrastierender
biochemischer Qualität (d.h., hohe Qualität Tithonia diversifolia (TD; C / N-Verhältnis: 13,
Lignin: 8.9%; Polyphenole: 1.7%), mittlere Qualität Calliandra calothyrsus (CC; 13, 13,.4) und
niedrige Qualität Zea mays (ZM; 59, 5.4, 1.2) auf die Nährstoffverfügbarkeit besser verstehen zu
können, wurden die Auswirkungen der Rückstandsqualität auf die Dynamik der relevanten
mikrobiellen Zersetzergemeinschaften untersucht. Zusätzlich wurde mineralischer N-Dünger
appliziert, um den mineralische N-Mangel zu kompensieren, der vor allem bei Ernterückständen
niedriger und mittlerer Qualität auftritt. Da N einer der am meist limitierenden Pflanzennährstoffe
in den Tropen ist, konzentriert sich die Studie auf Ammonium-oxidierende Prokaryoten, unter
Verwendung von DNA-basierter quantitativer PCR (qPCR) sowie des terminalen
Restriktionsfragmentlängenpolymorphismus (TRFLP). Darüber hinaus wurden die physikalisch-
chemischen Eigenschaften des Bodens gemessen und mit der Dynamik der mikrobiellen
Gemeinschaften verknüpft. Es wurde die Hypothese aufgestellt, dass der Bodentyp, aufgrund von
Unterschieden in Struktur und Nährstoffgehalt als auch die Saisonalität, welche die Bodenfeuchte
beeinflusst, die Reaktion der untersuchten mikrobiellen Gesellschaften auf die biochemische
Qualität der Ernterückstände bestimmen würde.
121
Im ersten Teil der Forschungsstudie (Kapitel 2) wurde die langfristige Reaktion der Abundanz und
Zusammensetzung aller Ammonium-oxidierenden Prokaryoten auf biochemisch kontrastierendes
organisches Material und deren Kombination mit mineralischem N Dünger in einem tonigen
Boden untersucht. Die Ergebnisse zeigten, dass durch die Zugabe qualitativ hochwertiger
organischer Einträge (TD), aufgrund des leicht verfügbaren N, die Abundanz aller untersuchten
Gene stimuliert wurde. Bei Zugabe von organischem Material mittlerer Qualität (CC) wurden sie
jedoch reduziert, was auf den hohen Polyphenol- und Ligninanteil des zugeführten Materials
zurückgeführt werden kann. Durch die Bildung von Polyphenol-Protein-Verbindungen, welche
organischen N vor mikrobieller Zersetzung schützen, wird somit ein N-Mangel herbeigeführt.
Weiterhin wurde eine unterschiedliche Zusammensetzung der AOA Gemeinschaft bei TD und CC
beobachtet. Die Nutzung von Ernterückständen geringer Qualität (ZM) verringerte die Abundanz
der AOB und der Gesamtarchaeenpopulation im Vergleich zur Behandlung TD wobei sich
außerdem eine unterschiedliche Zusammensetzung der AOA und AOB Gemeinschaften zwischen
den beiden Behandlungen ZM und TD zeigte. Die Förderung der AOA Abundanz in Behandlung
ZM kann der hohen Affinität der AOA zu Ammonium bei niedrigen N Verhältnissen
zugeschrieben werden. Alle Ernterückstände verringerten bei Kombination mit mineralischen N,
unabhängig von der Qualität, die Abundanz der untersuchten mikrobiellen Gemeinschaften mit
Ausnahme von AOB und veränderten deren Zusammensetzung mit Ausnahme von AOA. Dies
wurde auf die indirekte Reduktion des Boden pH-Werts durch den mineralischen Dünger
zurückgeführt, basierend auf der Tatsache dass AOB weniger sensibel auf Veränderungen des pH
Werts reagiert als AOA. Die Ergebnisse zeigen, dass die Qualität des Ernterückstands und
mineralischer N die Dynamik von AOA und AOB beeinflussen.
Aufbauend auf den Ergebnissen der ersten Studie im Lehmboden, wurden im zweiten Teil
(Kapitel 3) die saisonalen Effekte, besonders die Niederschläge, berücksichtigt. Um dies zu
erreichen wurden Daten aus zwei aufeinanderfolgenden, langen Regenzeiten der Jahre 2012 und
2013 integriert. Zwei unterschiedliche Wachstumsstadien (junges Stadium und Blütezeit) bei Zea
mays wurden ebenfalls berücksichtigt. Die Ergebnisse zeigten, dass die biochemische Qualität der
Ernterückstände die Abundanz von AOB, aber nicht von AOA Genen temporär beeinflusste.
Generell wurde ein ähnlicher Trend des Effekts von organischem Eintrag auf die AOB Abundanz
wie in Kapitel 2 deutlich, mit dem Unterschied, dass ZM die AOB Abundanz während der
Blütezeit von Mais erhöht hat. Dies war eine Bestätigung für die verzögerte Zersetzung von ZM
122
während der Saison aufgrund der N Limitierung. Die Verwendung von N-Dünger oder dessen
Kombination mit organischen Einträgen ergab ähnliche Ergebnisse wie in Kapitel 2 beschrieben.
Dies betonte die geringe Sensitivität der AOB Abundanz zu pH Wert Änderungen im Verleich zu
den AOA. Zusätzlich beeinflusste der Niederschlag alle untersuchten Gene mit Ausnahme der
AOA, ohne Bezug zur Qualität der Ernterückstände. Im Jahr 2013 konnte, im Vergleich zum Jahr
2012, eine Stimulation der AOB und Gesamtarchaeenpopulation festgestellt werden, während die
Gesamtbakterienpopulation zurück ging. Dies wurde mit den geringeren Niederschlägen in 2013
erklärt. Es wurde deshalb angenommen, dass das Optimum der wassergefüllten Poren (WFPS),
welches die Ausbreitung der AOB begünstigte, im Jahr 2013 erreicht wurde. Die
Gesamtarchaeenpopulation war an geringere Niederschläge angepasst, wobei höhere
Niederschläge im Jahr 2012 die Ausbreitung der Gesamtbakterienpopulation begünstigte.
Insgesamt zeigten die Ergebnisse, dass die Wechselbeziehung von Ernterückstandsqualität und
Saisonalität, die auch die Bodenfeuchte reguliert, zu einer Nischendifferenzierung zwischen AOB
und AOA beitrug.
Im dritten Teil dieser Forschungsarbeit (Kapitel 4) wurde zusätzlich zum tonigen Boden, der in
den vorangegangen Studien untersucht wurde, ein sandiger Boden miteinbezogen. Die beiden
kontrastierenden Böden zeigten spezifische Wechselbeziehungen mit der OBS hinsichtlich ihrer
Bodenstruktur, begünstigt durch ihren unterschiedlichen Gehalt an organischem Bodenkohlenstoff
(SOC). Es wurde daher die Hypothese aufgestellt, dass diese Unterschiede eine deutliche
Auswirkung auf die Dynamik der mikrobiellen Gemeinschaften haben. In lehmigen Böden bilden
Tonpartikel organisch-mineralische Verbindungen mit großen Oberflächen, die die Vermehrung
von Mikroorganismen und deren Schutz vor Prädatoren begünstigen. Diese Tatsache resultierte in
dieser Studie in einer höheren Abundanz der AOA und AOB unabhängig vom Ernterückstandstyp.
Andererseits haben sandige Böden eine grobe Struktur und bieten für frisch hinzugefügtes
organisches Material einen geringeren Schutz vor mikrobiellem Zugriff. Wechselwirkungen
zwischen der sandigen Bodentextur und der Qualität der organischen Rückstände förderten die
AOB Abundanz und veränderten die AOA Zusammensetzung. Dementsprechend förderten TD
(hoher organischer N-Gehalt) und ZM (hoher organischer C-Gehalt), im Vergleich zu CC mit
hohem Polyphenol- und Ligningehalt, die Abundanz von AOB und veränderten die AOA
Zusammensetzung. Wechselwirkungen des lehmigen Bodens mit organischen Ernteresten
veränderten die AOB Zusammensetzung. Dementsprechend zeigte sich eine Veränderung der
123
AOB Zusammensetzung zwischen den Varianten TD und ZM und zwischen CC und ZM. Diese
Ergebnisse implizierten, dass AOB auf N von CC zugreifen konnte und dass N die treibende Kraft
der veränderten AOB Zusammensetzung unter nicht limitierenden C Bedingungen (Lehmboden)
war. Im Gegensatz hierzu war die Veränderung der AOA Zusammensetzung nur möglich, wenn
C und N Substrate gleichzeitig limitierend waren, wie im Fall des untersuchten Sandbodens. Daher
sind Wechselwirkungen zwischen Bodentextur- und Ernterückstandseffekten spezifisch für die
mikrobielle Gruppe, wobei AOB sensitiver als AOA reagiert.
Insgesamt offenbaren die Ergebnisse dieser Doktorarbeit spezifische Reaktionen der Dynamik von
AOB und AOA auf die Qualität von organischen Ernterückständen und deren Kombinationen mit
mineralischen N-Dünger. Sie zeigt auch Auswirkungen der Wechselbeziehungen zwischen der
Qualität der Ernterückständen und Bodenbeschaffenheit als auch der Saisonabhängigkeit,
insbesondere Niederschlag, auf die Dynamik mikrobieller Gemeinschaften. Zukünftige
Untersuchungen der aktiven mikrobiellen Gemeinschaften durch Verwendung von RNA-basierten
Ansätzen müssen in Betracht gezogen werden, um unser Verständnis für den Einfluss der OBS-
Qualität auf die Bodennährstoffdynamik weiter zu verbessern.
124
References
African Development Bank. Building today a better Africa tomorrow.
http://www.afdb.org/en/topics-and-sectors/sectors/agriculture-agro-industries/african-
agriculture/
Ågren, G.I., Wetterstedt, J.Å.M., Billberger, M.F.K., 2012. Nutrient limitation on terrestrial plant
growth – modeling the interaction between nitrogen and phosphorus. New Phytologist 194,
953–960.
Allison, S.D., Lu, Y., Weihe, C., Goulden, M.L., Martiny, A.C., Treseder, K.K., Martiny, J.B.,
2013. Microbial abundance and composition influence litter decomposition response to
environmental change. Ecology 94, 714–725.
Anderson, M., Ter Braak, C.T., 2003. Permutation tests for multi-factorial analysis of variance.
Journal of Statistical Computation and Simulation 73, 85–113.
Anderson, M.J., 2001. A new method for non-parametric multivariate analysis of variance. Austral
Ecology 26, 32–46.
Anderson, M.J., Robinson, J., 2003. Generalized discriminant analysis based on distances.
Austrian and New Zealand Journal of Statistics 45, 301–318.
Angel, R., Soares, M.I.M., Ungar, E.D., Gillor, O., 2010. Biogeography of soil archaea and
bacteria along a steep precipitation gradient. The ISME Journal 4, 553–563.
Arp, D.J., Sayavedra-Soto, L.A., Hommes, N.G., 2002. Molecular biology and biochemistry of
ammonia oxidation by Nitrosomonas europaea. Archives of Microbiology 178, 250–255.
Avrahami, S., Bohannan, B.J.M., 2007. Response of Nitrosospira sp. strain AF-like ammonia
oxidizers to changes in temperature, soil moisture content, and fertilizer concentration.
Applied and Environmental Microbiology 73, 1166–1173.
Bates, S.T., Berg-Lyons, D., Caporaso, J.G., Walters, W.A., Knight, R., Fierer, N., 2011.
Examining the global distribution of dominant archaeal populations in soil. The ISME
Journal 5, 908–917.
Bationo, A., Waswa, B., Abdou, A., Bado, B.V., Bonzi, M., Iwuafor, E., Kibunja, C., Kihara, J.,
Mucheru, M., Mugendi, D., Mugwe, J., Mwale, C., Okeyo, J., Olle, A., Roing, K., Sedogo,
M., 2012. Overview of long-term experiments in Africa, in: Bationo, A., Waswa, B.,
Kihara, J., Adolwa, I., Vanlauwe, B., Saidou, K. (Eds.), Lessons learned from long-term
125
soil fertility management experiments in Africa. Springer Netherlands, Dordrecht, pp. 1–
26.
Belnap, J., 2001. Factors influencing nitrogen fixation and nitrogen release in biological soil crusts.
Ecological Studies. 150, 241–261.
Blainey, P.C., Mosier, A.C., Potanina, A., Francis, C.A., Quake, S.R., 2011. Genome of a low-
salinity ammonia-oxidizing archaeon determined by single-cell and metagenomic analysis.
PLoS ONE 6, 71–97.
Boj, E., Fortiana, J., Esteve, A., Claramunt, M.M., Costa, T., 2011. Actuarial applications of
distance-based generalized linear models. ASTIN pp 1–12.
Bossuyt, H., Denef, K., Six, J., Frey, S.D., Merckx, R., Paustian, K., 2001. Influence of microbial
populations and residue quality on aggregate stability. Applied Soil Ecology 16, 195–208.
Bru, D., Ramette, A., Saby, N.P.A., Dequiedt, S., Ranjard, L., Jolivet, C., Arrouays, D., Philippot,
L., 2011. Determinants of the distribution of nitrogen-cycling microbial communities at
the landscape scale. The ISME Journal 5, 532–542.
Bürgmann, H., Meier, S., Bunge, M., Widmer, F., Zeyer, J., 2005. Effects of model root exudates
on structure and activity of a soil diazotroph community. Environmental Microbiology 7,
1711–1724.
Bustin, S.A., 2005. Real-time reverse transcription PCR. Encycl. Diagn. Genomics Proteomics
1131–1135.
Cadisch, G., Imhof, H., Urquiaga, S., Boddey, R.M., Giller, K.E., 1996. Carbon (13C) and nitrogen
mineralization potential of particulate light organic matter after rainforest clearing. Soil
Biology and Biochemistry. 28, 1555–1567.
Che, J., Zhao, X.Q., Zhou, X., Jia, Z.J., Shen, R.F., 2015. High pH-enhanced soil nitrification was
associated with ammonia-oxidizing bacteria rather than archaea in acidic soils. Applied
Soil Ecology 85, 21–29.
Chen, X.-P., Zhu, Y.-G., Xia, Y., Shen, J.-P., He, J.-Z., 2008. Ammonia-oxidizing archaea:
important players in paddy rhizosphere soil? Environmental Microbiology 10, 1978–1987.
Chèneby, D. Bru, D. Pascault,N. Maron,P.,A. Ranjard,L.and Philippot, L., 2010. Role of plant
residues in determining temporal patterns of the activity, size, and structure of nitrate
reducer communities in soil. Applied and Environmental Microbiology 76, 7136–7143.
126
Chivenge, P., Vanlauwe, B., Gentile, R., Six, J., 2011. Organic resource quality influences short-
term aggregate dynamics and soil organic carbon and nitrogen accumulation. Soil Biology
and Biochemistry 43, 657–666.
Chivenge, P., Vanlauwe, B., Gentile, R., Wangechi, H., Mugendi, D., van Kessel, C., Six, J., 2009.
Organic and mineral input management to enhance crop productivity in central Kenya.
Agronomy Journal 101, 1266–1275.
Clarke, K. R., Gorley, R.N., 2001. Primerv5: User Manual/Tutorial. PRIMER-E, Plymouth, UK.
Clarke, K.R., 1993. Non-parametric multivariate analyses of changes in community structure.
Australian Journal of Ecology. 18, 117–143.
Cray, J.A., Russell, J.T., Timson, D.J., Singhal, R.S., Hallsworth, J.E., 2013. A universal measure
of chaotropicity and kosmotropicity: A universal measure of chao- and kosmotropicity.
Environmental Microbiology 15, 287–296.
Cray, J.A., Stevenson, A., Ball, P., Bankar, S.B., Eleutherio, E.C.A., Ezeji, T.C., Singhal, R.S.,
Thevelein, J.M., Timson, D.J., Hallsworth, J.E., 2015. Chaotropicity: a key factor in
product tolerance of biofuel-producing microorganisms. Current Opinion in Biotechnology
33, 228–259.
De Boer, W., and Kowalchuk, G. A., 2001. Nitrification in acid soils: microorganisms and
mechanisms. Soil Biology and Biochemistry 33, 853–866.
De Gannes, V., Eudoxie, G., Hickey, W.J., 2014. Impacts of edaphic factors on communities of
ammonia-oxidizing archaea, ammonia-oxidizing bacteria and nitrification in tropical soils.
PLoS ONE 9, 1–14.
De Lima Alves, F., Stevenson A., Baxter, E., Gillion, J.L.M., Hejazi, F., Hayes, S., Morrison,
I.E.G., Prior, B.A., McGenity, T.J., Rangel, D.E.N., Magan, N., Timmis, K.N., Hallsworth,
J.E., 2015. Concomitant osmotic and chaotropicity-induced stresses in Aspergillus wentii:
compatible solutes determine the biotic window. Current Genetics 61, 457–477.
Deutzmann, J.S., Wörner, S., Schink, B., 2011. Activity and diversity of methanotrophic bacteria
at methane seeps in eastern lake Constance sediments. Applied and Environmental
Microbiology 77, 2573–2581.
Di, H.J., Cameron, K.C., Shen, J.-P., Winefield, C.S., O’Callaghan, M., Bowatte, S., He, J.-Z.,
2010. Ammonia-oxidizing bacteria and archaea grow under contrasting soil nitrogen
conditions. FEMS Microbiology Ecology 72, 386–394.
127
Di, H.J., Cameron, K.C., Shen, J.P., Winefield, C.S., O’Callaghan, M., Bowatte, S., He, J.Z., 2009.
Nitrification driven by bacteria and not archaea in nitrogen-rich grassland soils. Nature
Geoscience 2, 621–624.
Dieckow, J., Bayer, C., Conceição, P.C., Zanatta, J.A., Martin-Neto, L., Milori, D.B.M., Salton,
J.C., Macedo, M.M., Mielniczuk, J., Hernani, L.C., 2009. Land use, tillage, texture and
organic matter stock and composition in tropical and subtropical Brazilian soils. European
Journal of Soil Science 60, 240–249.
Dunbar, J., Ticknor, L.O., Kuske, C.R., 2001. Phylogenetic specificity and reproducibility and new
method for analysis of terminal restriction fragment profiles of 16S rRNA genes from
bacterial communities. Applied and Environmental Microbiology 67, 190–197.
Edwards, U., Rogall, T., Blöcker, H., Emde, M., Böttger, E.C., 1989. Isolation and direct complete
nucleotide determination of entire genes. Characterization of a gene coding for 16S
ribosomal RNA. Nucleic Acids Research 17, 7843–7853.
Enwall, K., Hallin, S., 2009. Comparison of TRFLP and DGGE techniques to assess denitrifier
community composition in soil. Letters in Applied Microbiology 48, 145–148.
Erguder, T.H., Boon, N., Wittebolle, L., Marzorati, M., Verstraete, W., 2009. Environmental
factors shaping the ecological niches of ammonia-oxidizing archaea. FEMS Microbiology
Reviews 33, 855–869.
España, M., Rasche, F., Kandeler, E., Brune, T., Rodriguez, B., Bending, G.D., Cadisch, G., 2011.
Identification of active bacteria involved in decomposition of complex maize and soybean
residues in a tropical vertisol using 15N-DNA stable isotope probing. Pedobiologia 54, 187–
193.
FAO, 2006. World Reference Base for Soil Resources, FAO, Rome, Italy.
FAO, 2008. World reference base for soil resources, FAO, Rome, Italy.
FAO, 2012. Current world fertilizer trends and outlook to 2016, FAO, Rome, Italy.
Feinstein, L.M., Sul, W.J., Blackwood, C.B., 2009. Assessment of Bias Associated with
Incomplete Extraction of Microbial DNA from Soil. Applied and Environmental
Microbiology 75, 5428–5433.
Fontaine, S., Mariotti, A., Abbadie, L., 2003. The priming effect of organic matter: a question of
microbial competition? Soil Biology and Biochemistry 35, 837–843.
128
Fonte, S.J., Yeboah, E., Ofori, P., Quansah, G.W., Vanlauwe, B., Six, J., 2009. Fertilizer and
residue quality effects on organic matter stabilization in soil aggregates. Soil Science
Society of America Journal 73, 961–966.
Francis, C.A., Roberts, K.J., Beman, J.M., Santoro, A.E., Oakley, B.B., 2005. Ubiquity and
diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean.
Proceedings of the National Academy of Sciences of the United States of America 102,
14683–14688.
Galloway, J.N., Townsend, A.R., Erisman, J.W., Bekunda, M., Cai, Z., Freney, J.R., Martinelli,
L.A., Seitzinger, S.P., Sutton, M.A., 2008. Transformation of the nitrogen cycle: recent
trends, questions, and potential solutions. Science 320, 889–892.
Garbeva, P., van Veen, J.A., van Elsas, J.D., 2004. Microbial diversity in soil: Selection of
microbial populations by plant and soil type and implications for disease suppressiveness.
Annual Review of Phytopathology 42, 243–270.
Geisseler, D., Scow, K.M., 2014. Long-term effects of mineral fertilizers on soil microorganisms
– A review. Soil Biology and Biochemistry 75, 54–63.
Gelsomino, A., Keijzer-Wolters, A.C., Cacco, G., van Elsas, J.D., 1999. Assessment of bacterial
community structure in soil by polymerase chain reaction and denaturing gradient gel
electrophoresis. Journal of Microbiological Methods 38, 1–15.
Gentile, R., Vanlauwe, B., Kavoo, A., Chivenge, P., Six, J., 2011a. Residue quality and N fertilizer
do not influence aggregate stabilization of C and N in two tropical soils with contrasting
texture. Nutrient Cycling in Agroecosystems 88, 121–131.
Gentile, R., Vanlauwe, B., Six, J., 2011b. Litter quality impacts short-but not long-term soil carbon
dynamics in soil aggregate fractions. Ecological Applications 21, 695–703.
Gentile, R., Vanlauwe, B., van Kessel, C., Six, J., 2009. Managing N availability and losses by
combining fertilizer-N with different quality residues in Kenya. Agriculture Ecosystems
and Environments 131, 308–314.
Girvan, M.S., Bullimore, J., Pretty, J.N., Osborn, A.M., Ball, A.S., 2003. Soil type is the primary
determinant of the composition of the total and active bacterial communities in arable soils.
Applied and Environmental Microbiology 69, 1800–1809.
129
Gleeson, D.B., Müller, C., Banerjee, S., Ma, W., Siciliano, S.D., Murphy, D.V., 2010. Response
of ammonia-oxidizing archaea and bacteria to changing water filled pore space. Soil
Biology and Biochemistry 42, 1888–1891.
Gubry-Rangin, C., Nicol, G. W., Prosser, J. I., 2010. Archaea rather than bacteria control
nitrification in two agricultural acidic soils. FEMS Microbiology Ecology 74, 566–574.
Hagerman, A.E., 2012. Fifty years of polyphenol–protein complexes, in: Cheynier, V., Sarni-
Manchado, P., Quideau, S. (Eds.), Recent advances in polyphenol research. Wiley-
Blackwell, pp. 71–97.
Hai, B., Diallo, N.H., Sall, S., Haesler, F., Schauss, K., Bonzi, M., Assigbetse, K., Chotte, J.-L.,
Munch, J.C., Schloter, M., 2009. Quantification of key genes steering the microbial
nitrogen cycle in the rhizosphere of sorghum cultivars in tropical agroecosystems. Applied
and Environmental Microbiology 75, 4993–5000.
Hallin, S., Jones, C.M., Schloter, M., Philippot, L., 2009. Relationship between N-cycling
communities and ecosystem functioning in a 50-year-old fertilization experiment. The
ISME Journal 3, 597–605.
Hallsworth, J.E., Heim, S., Timmis, K.N., 2003. Chaotropic solutes cause water stress in
Pseudomonas putida. Environmental Microbiology 5,1270–1280.
Hatzenpichler, R., Lebedeva, E.V., Spieck, E., Stoecker, K., Richter, A., Daims, H., Wagner, M.,
2008. A moderately thermophilic ammonia-oxidizing crenarchaeote from a hot spring.
Proceedings of the National Academy of Sciences 105, 2134–2139.
Hazelton, P., Murphy, B., 2007. Interpreting soil test results: What do all the numbers mean?,
Second edition. Csiro Publishing, Collingwood VIC, Australia.
He, J., Shen, J., Zhang, L., Zhu, Y., Zheng, Y., Xu, M., Di, H., 2007. Quantitative analyses of the
abundance and composition of ammonia-oxidizing bacteria and ammonia-oxidizing
archaea of a Chinese upland red soil under long- term fertilization practices. Environmental
Microbiology 9, 2364–2374.
Heal, O.W., Anderson, J.M., Swift, M.J., 1997. Plant litter quality and decomposition: an historical
overview. In: Cadisch, G., Giller, K.E. (eds.), Driven by nature: Plant litter quality and
decomposition. CAB International, Wallingford, pp. 3–30.
Henao, J., Baanante, C., 2006. Agricultural Production and soil nutrient mining in Africa:
Implications for resource conservation and policy development. Muscle Shoals, Alabama,
130
U.S.A. An International Center for Soil Fertility and Agricultural Development.
www.ifdc.org
Hepburn, J. S., 1908. The modifications of the Kjeldahl method for the quantitative determination
of nitrogen. Franklin Institute 166: 81-89.
Höfferle, S., Nicol, G. W., Pal, L., Hacin, J., Prosser, J. I., Mandic-Mulec, I., 2010. Ammonium
supply rate influences archaeal and bacterial ammonia oxidizers in a wetland soil vertical
profile. FEMS Microbiology Ecology 74, 302–315.
Hofmockel, S. K., Fierer, N., Benjamin, P. C. and Robert B. J., 2010. Amino acid abundance and
proteolytic potential in North. American soils ecosystem ecology. Oecologia 10, 1601–
1609.
Hugenholtz, P., 2002. Exploring prokaryotic diversity in the genomic era. Genome Biology 3, 1–
3.
Hyman, M.R., Arp, D.J., 1992. 14C2H2- and 14CO2-labeling studies of the de novo synthesis of
polypeptides by Nitrosomonas europaea during recovery from acetylene and light
inactivation of ammonia monooxygenase. Journal of Biological Chemistry 267, 1534–
1545.
ICRAF, 1999. Laboratory methods for soil and plant analysis. International Centre for Research
in Agroforestry, Nairobi, Kenya.
Isobe, K., Koba, K., Suwa, Y., Ikutani, J., Fang, Y., Yoh, M., Mo, J., Otsuka, S., Senoo, K., 2012.
High abundance of ammonia-oxidizing archaea in acidified subtropical forest soils in
southern China after long-term N deposition. FEMS Microbiology Ecology 80, 193–203.
Jia, Z., Conrad, R., 2009. Bacteria rather than Archaea dominate microbial ammonia oxidation in
an agricultural soil. Environmental Microbiology 11, 1658–1671.
Jones A, Breuning-Madsen H, Deckers J, Dewitte O, Gallali T, Le Roux, P., Michéli, E.,
Montanarella, L., Spaargaren, O., Thiombiano, L., Van Ranst, E., Yemefack, M.,
Zougmore, R., 2013. Soil Atlas of Africa.
http://eusoils.jrc.ec.europa.eu/library/maps/africa_atlas/
Jones, E., Singh, B., 2014. Organo-mineral interactions in contrasting soils under natural
vegetation. Frontiers in Environmental Science 2, 1–15.
Junier, P., Molina, V., Dorador, C., Hadas, O., Kim, O.S., Junier, T., Witzel, K.P., Imhoff, J.F.,
2010. Phylogenetic and functional marker genes to study ammonia-oxidizing
131
microorganisms (AOM) in the environment. Applied Microbiology and Biotechnology 85,
425–440.
Kaewpradit, W., Toomsan, B., Vityakon, P., Limpinuntana, V., Saenjan, P., Jogloy, S., Patanothai,
A., Cadisch, G., 2008. Regulating mineral N release and greenhouse gas emissions by
mixing groundnut residues and rice straw under field conditions. Europen Journal of Soil
Science 59, 640–652.
Kamaa, M.M., Mburu, H.N., Blanchart, E., Chibole, L., Chotte, J.L., Kibunja, C.N., Lesueur, D.,
2012. Effects of organic and inorganic fertilization on soil bacterial and fungal microbial
diversity in the Kabete long-term trial, Kenya. Biology and Fertility of Soils 4, 315–321.
Kamolmanit, B., Vityakon, P., Kaewpradit, W., Cadisch, G., Rasche, F., 2013. Soil fungal
communities and enzyme activities in a sandy, highly weathered tropical soil treated with
biochemically contrasting organic inputs. Biology and Fertility of Soils 49, 905–917.
Kanerva, S., Kitunen, V., Kiikkilä, O., Loponen, J., Smolander, A., 2006. Response of soil C and
N transformations to tannin fractions originating from Scots pine and Norway spruce
needles. Soil Biology and Biochemistry 38, 1364–1374.
Ke, X., Lu, Y., 2012. Adaptation of ammonia-oxidizing microbes to environment shift of paddy
field soil. FEMS Microbiology Ecology 80, 87–97.
Keil, D., Meyer, A., Berner, D., Poll, C., Schützenmeister, A., Piepho, H.-P., Vlasenko, A.,
Philippot, L., Schloter, M., Kandeler, E., Marhan, S., 2011. Influence of land-use intensity
on the spatial distribution of N-cycling microorganisms in grassland soils. FEMS
Microbiology Ecology 77, 95–106.
Kibunja, C.N., 2007. Impact of long-term application of organic and inorganic nutrient sources in
maize-bean rotation to soil nitrogen dynamics and soil microbial populations and activities.
PhD thesis, University of Nairobi, Kenya.
Kirschbaum, M. U., 1995. The temperature dependence of soil organic matter decomposition, and
the effect of global warming on soil organic C storage. Soil Biology and Biochemistry 27,
753–760.
Kögel-Knabner, I., Guggenberger, G., Kleber, M., Kandeler, E., Kalbitz, K., Scheu, S., Eusterhues,
K., Leinweber, P., 2008. Organo-mineral associations in temperate soils: Integrating
biology, mineralogy, and organic matter chemistry. Journal of Plant Nutrition and Soil
Science 171, 61–82.
132
Kononova, M.M., (1966). Soil Organic Matter. Pergamon Press, New York, p.544.
Konstantinidis, K.T., Ramette, A., Tiedje, J.M., 2006. The bacterial species definition in the
genomic era. Philosophical Transactions of the Royal Society B: Biological Science 361,
1929–1940.
Kraus, T.E.C., Dahlgren, R.A., Zasoski, R.J., 2003. Tannins in nutrient dynamics of forest
ecosystems - a review. Plant Soil 256, 41–66.
Krull, E., Baldock, J., Skjemstad, J., others, 2001. Soil texture effects on decomposition and soil
carbon storage, in: Net Ecosystem Exchange CRC Workshop Proceedings. pp. 103–110.
Kunlanit, B., Vityakon, P.,Puttaso, A., Cadisch, G., Rasche, F., 2014. Mechanisms controlling soil
organic carbon composition pertaining to microbial decomposition of biochemically
contrasting organic residues: Evidence from midDRIFTS peak area analysis. Soil Biology
and Biochemistry 76, 100–108.
Lane, D, 1991. 16S/23S rRNA sequencing, In: Stackebrandt A Goodfello M (eds) Nucleic acid
techniques systematics, 1st ed. Wiley, West Sussex, UK pp 115-257, Chichester ; New
York.
Legendre, P., Anderson, J.M., 1999. Distance-based redundancy analysis: testing multispecies
responses in multifactorial ecological experiments. Ecological Monographs 69, 1–24.
Leininger, S., Urich, T., Schloter, M., Schwark, L., Qi, J., Nicol, G.W., Prosser, J.I., Schuster, S.C.,
Schleper, C., 2006. Archaea predominate among ammonia-oxidizing prokaryotes in soils.
Nature 442, 806–809.
Levičnik-Hofferle, Š., Nicol, G.W., Ausec, L., Mandić-Mulec, I., Prosser, J.I., 2012. Stimulation
of thaumarchaeal ammonia oxidation by ammonia derived from organic nitrogen but not
added inorganic nitrogen. FEMS Microbiology Ecology 80, 114–123.
Liu, D.L., Helyar, K.R., Conyers, M.K., Fisher, R., Poile, G.J., 2004. Response of wheat, triticale
and barley to lime application in semi-arid soils. Field Crops Research 90, 287–301.
Liu, W.T., Marsh, T.L., Cheng, H., Forney, L.J., 1997. Characterization of microbial diversity by
determining terminal restriction fragment length polymorphisms of genes encoding 16S
rRNA. Applied and Environmental Microbiology 63, 4516–4522.
Lucas, S.T., (2013) Managing soil microbial communities with organic amendments to promote
soil aggregate formation and plant health. Thesis and dissertations--plant and soil sciences.
http://uknowledge.uky.edu/pss_etds/24.
133
Lueders, T., Friedrich, M., 2000. Archaeal population dynamics during sequential reduction
processes in rice field soil. Applied and Environmental Microbiology 66, 2732–2742.
Magdalena, F. Stefania, J., 2011. Agricultural utilization of dairy sewage sludge: Its effect on
enzymatic activity and microorganisms of the soil environment. African Journal of
Microbiology Research 5, 1755–1762.
Magill, A.H., Aber, J.D., 2000. Variation in soil net mineralization rates with dissolved organic
carbon additions. Soil Biology and Biochemistry 32, 597–601.
Mando, A., Bonzi, M., Wopereis, M.C.S., Lompo, F., Stroosnijder, L., 2005. Long-term effects of
mineral and organic fertilization on soil organic matter fractions and sorghum yield under
Sudano-Sahelian conditions. Soil Use and Managemenet. 21, 396–401.
Mapfumo, P., Mtambanengwe, F., Vanlauwe, B., 2007. Organic matter quality and management
effects on enrichment of soil organic matter fractions in contrasting soils in Zimbabwe.
Plant Soil 296, 137–150.
Martens-Habbena, W., Berube, P.M., Urakawa, H., de la Torre, J.R., Stahl, D.A., 2009. Ammonia
oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature
461, 976–979.
Martens-Habbena, W., Stahl, D.A., 2011. Nitrogen metabolism and kinetics of ammonia-oxidizing
archaea. Methods in Enzymology.496, 465–487.
Mcardle, B.H., Anderson, M.J., 2001. Fitting multivariate models to community data: A comment
on distance-based redundancy analysis. Ecology 83, 290–297.
Millar, N., Baggs, E.M., 2004. Chemical composition, or quality, of agroforestry residues
influences N2O emissions after their addition to soil. Soil Biology and Biochemistry 36,
935–943.
Milling, A., Smalla, K., Maidl, F.X., Schloter, M., Munch, J.C., 2005. Effects of transgenic
potatoes with an altered starch composition on the diversity of soil and rhizosphere bacteria
and fungi. Plant Soil 266, 23–39.
Mills, D.K., Entry, J.A., Gillevet, P.M., Mathee, K., 2007. Assessing microbial community
diversity using amplicon length heterogeneity polymerase chain reaction. Soil Science
Society of America Journal 71, 572–578.
Morimoto, S., Hayatsu, M., Hoshino, Y.T, Nagaoka, K., Yamazaki, M., Karasawa, T., Takenaka,
M., Akiyama, H., 2011. Quantitative analyses of ammonia-oxidizing archaea (AOA) and
134
ammonia-oxidizing bacteria (AOB) in fields with different soil types. Microbes and
Environments 26, 248–253.
Mrkonjic Fuka, M., Engel, M., Gattinger, A., Bausenwein, U., Sommer, M., Munch, J. C. and
Schloter, M . 2008. Factors influencing variability of proteolytic genes and activities in
arable soils. Soil Biology and Biochemistry 40,1646–1653.
Mrkonjic Fuka, M., Engel, M., Hagn, A., Munch, J.C., Sommer, M., Scholoter, M., 2009. Changes
of diversity pattern of proteolytic bacteria over time and space in an agricultural soil.
Microbial Ecology 57, 391–401.
Mtambanengwe, F., Mapfumo, P., Vanlauwe, B., 2006. Comparative short-term effects of
different quality organic resources on maize productivity under two different environments
in Zimbabwe. Nutrient Cycling in Agroecosystems. 76, 271–284.
Muema, E.K., Cadisch G., Röhl, C., Vanlauwe, B., Rasche, F., 2015. Response of ammonia-
oxidizing bacteria and archaea to biochemical quality of organic inputs combined with
mineral nitrogen fertilizer in an arable soil. Applied Soil Ecology 95, 128–139.
Musyoki, M.K., Cadisch, G., Enowashu, E., Zimmermann, J., Muema, E., Beed, F., Rasche, F.,
2015. Promoting effect of Fusarium oxysporum [f.sp. strigae] on abundance of nitrifying
prokaryotes in a maize rhizosphere across soil types. Biological Control 83, 37–45.
Mutabaruka, R., Hairiah, K., Cadisch, G., 2007. Microbial degradation of hydrolysable and
condensed tannin polyphenol–protein complexes in soils from different land-use histories.
Soil Biology and Biochemistry 39, 1479–1492.
Muyzer, G., 1999. Genetic fingerprinting of microbial communities-present status and future
perspectives, in: Proceedings of the 8th International Symposium on Microbial Ecology.
pp. 1–10.
Muyzer, G., de Waal, E.C., Uitterlinden, A.G., 1993. Profiling of complex microbial populations
by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified
genes coding for 16S rRNA. Applied and Environmental Microbiology 59, 695–700.
Myers, R.J.K., Palm, C.A., Cuevas, E., Gunatilleke, I.U.N., Brossard. M., 1994. The
synchronization of nutrient mineralization and plant nutrient demand. In: Woomer, P.L.,
Swift, M.J. (eds.), The biological management of tropical soil fertility. John Wiley & Sons,
Chichester, UK.
135
Nannipieri, P., Ascher, J., Ceccherini, M., Landi, L., Pietramellara, G., Renella, G., 2003.
Microbial diversity and soil functions. Europen Journal of Soil Science 54, 655–670.
Neumann, D., Heuer, A., Hemkemeyer, M., Martens, R., Tebbe, C.C., 2013. Response of
microbial communities to long-term fertilization depends on their microhabitat. FEMS
Microbiology Ecology 86, 71–84.
Nicol, G. W., Leininger, S., Schleper, C., Prosser, J. I., 2008. The influence of soil pH on the
diversity, abundance and transcriptional activity of ammonia-oxidizing archaea and
bacteria. Environmental Microbiology 10, 2966–2978.
Nicol, G.W., Schleper, C., 2006. Ammonia-oxidising Crenarchaeota: important players in the
nitrogen cycle? Trends in Microbiology 14, 207–212.
Norton, J. M., Stark, J. M., 2011. Regulation and measurement of nitrification in terrestrial
systems. Methods in Enzymology. 486, 343–368.
Nziguheba, G., Merckx, R., Palm, C.A., 2005. Carbon and nitrogen dynamics in a phosphorus-
deficient soil amended with organic residues and fertilizers in western Kenya. Biology and
Fertility of Soils 41, 240–248.
Okur, N., Altindişl, A., Cengel, M., Gocmez, S., Kayikcioğlu, H.H., 2009. Microbial biomass and
enzyme activity in vineyard soils under organic and conventional farming systems. Turkey
Journal of Agriculture and Forestry 33, 413–423.
Onwonga, R.N., Lelei, J.J., Mochoge, B.B., 2010. Mineral nitrogen and microbial biomass
dynamics under different acid soil management practices for maize production. Journal of
Agricultural Science 2, 16–30.
Palm, C.A., Gachengo, C.N., Delve, R.J., Cadisch, G., Giller, K.E., 2001a. Organic inputs for soil
fertility management in tropical agroecosystems: application of an organic resource
database. Agriculture, Ecosystems and Environment 83, 27–42.
Palm, C.A., Giller, K.E., Mafongoya, P.L., Swift, M.J., 2001b. Management of organic matter in
the tropics: translating theory into practice. Nutrient Cycling in Agroecosystems 61, 63–
75.
Palm, C.A., Myers, R. J. K., Nandwa, S., 1997. Organic-inorganic nutrient interactions in soil
fertility replenishment. In: Buresh, R.J., Sanchez, A., Calhoun, F.G. (eds.), Replenishing
soil fertility in Africa. ASA-SSSA Special publication 51, 193–218.
136
Pandey, J., Ganesan, K., Jain, R.K., 2007. Variations in T-RFLP profiles with differing chemistries
of fluorescent dyes used for labeling the PCR primers. Journal of Microbiological Methods
68, 633–638.
Partey, S.T., Preziosi, R.F., Robson, G.D., 2013. Maize residue interaction with high quality
organic materials: Effects on decomposition and nutrient release dynamics. Agricultural
Research 2, 58–67.
Pereira e Silva, M.C., Poly, F., Guillaumaud, N., van Elsas, J.D., Falcão Salles, J., 2012.
Fluctuations in ammonia-oxidizing communities across agricultural soils are driven by soil
structure and pH. Terrestrial Microbiology 3, 1–22.
Peter, M.V., John, D., Robert, W.H., Gene, E.L., Pamela, A., David, W.S., William, H.S., David,
G., 1997. Human alteration of the global nitrogen cycle: sources and consequences.
Ecological applications 7, 737–750.
Petersen, D.G., Blazewicz, S.J., Firestone, M., Herman, D.J., Turetsky, M., Waldrop, M., 2000.
Abundance of microbial genes associated with nitrogen cycling as indices of
biogeochemical process rates across a vegetation gradient in Alaska. Environmental
Microbiology 14, 993–1008.
Piepho, H,P., 2009. Data transformation in statistical analysis of field trials with changing
treatment variance. Agronomy Journal 101, 865–869.
Poll, C., Thiede, A., Wermbter, N., Sessitsch, A., Kandeler, E., 2003. Micro-scale distribution of
microorganisms and microbial enzymeactivities in a soil with long-term organic
amendment. Europen Journal of Soil Science54, 715–724.
Population reference bureau, 2015. http://www.prb.org/Publications/Articles/2015/sahel-
demographics.aspx
Prosser, J.I., Nicol, G.W., 2008. Relative contributions of archaea and bacteria to aerobic ammonia
oxidation in the environment. Environmental Microbiology 10, 2931–2941.
Prosser, J.I., Nicol, G.W., 2012. Archaeal and bacterial ammonia-oxidisers in soil: the quest for
niche specialisation and differentiation. Trends in Microbiology 20, 523–531.
Puttaso, A., Vityakon, P., Rasche, F., Saenjan, P., Treloges, V., Cadisch, G., 2013. Does organic
residue quality influence carbon retention in a tropical sandy soil? Soil Science Society of
America Journal 77, 1001.
137
Puttaso, A., Vityakon, P., Saenjan, P., Trelo-ges, V., Cadisch, G., 2011. Relationship between
residue quality, decomposition patterns, and soil organic matter accumulation in a tropical
sandy soil after 13 years. Nutrient Cycling in Agroecosystems 89, 159–174.
Rasche, F., Cadisch, G., 2013. The molecular microbial perspective of organic matter turnover and
nutrient cycling in tropical agroecosystems - What do we know? Biology and Fertility of
Soils 49, 251–262.
Rasche, F., Hödl, V., Poll, C., Kandeler, E., Gerzabek, M.H., Van Elsas, J.D., Sessitsch, A., 2006a.
Rhizosphere bacteria affected by transgenic potatoes with antibacterial activities compared
with the effects of soil, wild-type potatoes, vegetation stage and pathogen exposure. FEMS
Microbiology Ecology 56, 219–235.
Rasche, F., Knapp, D., Kaiser, C., Koranda, M., Kitzler, B., Zechmeister-Boltenstern, S., Richter,
A., Sessitsch, A., 2011. Seasonality and resource availability control bacterial and archaeal
communities in soils of a temperate beech forest. The ISME Journal 5, 389–402.
Rasche, F., Musyoki, M.K., Röhl, C., Muema, E.K., Vanlauwe, B., Cadisch, G., 2014. Lasting
influence of biochemically contrasting organic inputs on abundance and community
structure of total and proteolytic bacteria in tropical soils. Soil Biology and Biochemistry
74, 204–213.
Rasche, F., Trondl, R., Naglreiter, C., Reichenauer, T.G., Sessitsch, A., 2006b. Chilling and
cultivar type affect the diversity of bacterial endophytes colonizing sweet pepper
(Capsicum anuum L.). Canadian Journal of Microbiology 52, 1036–1045.
Rastogi, G., Sani, R.K., 2011. Molecular techniques to assess microbial community structure,
function, and dynamics in the environment, in: Ahmad, I., Ahmad, F., Pichtel, J. (Eds.),
Microbes and Microbial Technology. Springer New York, New York, NY, pp. 29–57.
Rees, G.N., Baldwin, D.S., Watson, G.O., Perryman, S., Nielsen, D.L., 2005. Ordination and
significance testing of microbial community composition derived from terminal restriction
fragment length polymorphisms: application of multivariate statistics. Antonie Van
Leeuwenhoek 86, 339–347.
Rotthauwe, J.H., Witzel, K.P., Liesack, W., 1997. The ammonia monooxygenase structural gene
amoA as a functional marker: molecular fine-scale analysis of natural ammonia-oxidizing
populations. Applied and Environmental Microbiology 63, 4704–4712.
138
Sądej, W., Przekwas, K., 2008. Fluctuations of nitrogen levels in soil profile under conditions of
a long-term fertilization experiment. Plant Soil Environ 54: 197–203.
Sakala,W., G. Cadisch, and K.E., Giller., 2000. Interactions between residues of maize and
pigeonpea and mineral N fertilizers during decomposition and N mineralization. Soil
Biology and Biochemistry 32, 679–688.
Sakurai, M., Suzuki, K., Onodera, M. Shinano, T., Osaki, M., 2007. Analysis of bacterial
communities in soil by PCR–DGGE targeting protease genes. Soil Biology and
Biochemistry 39, 2777–2784.
Samahadthai, P., Vityakon, P., Saenjan, P., 2010. Effects of different quality plant residues on soil
carbon accumulation and aggregate formation in a tropical sandy soil in Northeast Thailand
as revealed by a 10-year field experiment. Land Degradation and Development 21, 463–
473.
Sanchez, P.A., Jama, B. A., 2002. Soil fertility replenishment takes off in east and southern Africa.
In: Vanlauwe, B., Diels, J., Sanginga, N., Merckx, R. (eds.), Integrated plant nutrient
management in Sub-Saharan Africa: from concept to practice. CAB International,
Wallingford, Oxon.
Sardans, J. and Penuelas, J., 2005. Drought decreases soil enzyme activity in a Mediterranean
Quercus ilex L. forest. Soil Biology and Biochemistry 37, 455–461.
SAS Institute, 2014. SAS system for windows, Version 9.3. SAS Institute Inc. Cary, NC, USA.
Schauss, K., Focks, A., Leininger, S., Kotzerke, A., Heuer, H., Thiele-Bruhn, S., Sharma, S.,
Wilke, B.-M., Matthies, M., Smalla, K., Munch, J.C., Amelung, W., Kaupenjohann, M.,
Schloter, M., Schleper, C., 2009. Dynamics and functional relevance of ammonia-
oxidizing archaea in two agricultural soils. Environmental Microbiology 11, 446–456.
Schematic representation of the flow of nitrogen through the environment.
https://en.wikipedia.org/wiki/Nitrogen_cycle
Schleper, C., 2010. Ammonia oxidation: different niches for bacteria and archaea? The ISME
Journal 4, 1092–1094.
Schleper, C., Jurgens, G., Jonuscheit, M., 2005. Genomic studies of uncultivated archaea. Nature
Reviews Microbiology 3, 479–488.
139
Schmidt, M.A., Kreinberg, A.J., Gonzalez, J.M., Halvorson, J.J., French, E., Bollmann, A.,
Hagerman, A.E., 2013. Soil microbial communities respond differently to three chemically
defined polyphenols. Plant Physiology and Biochemistry 72, 190–197.
Schulz, E., 2004. Influence of site conditions and management on different soil organic matter
(som) pools. Archives of Agronomy and Soil Science 50, 33–47.
Schulz, E., Körschens, M., 1998. Characterization of the decomposable part of soil organic matter
(som) and transformation processes by hot water extraction. Eurasian Soil Science 31, 809–
813.
Schütte, U.M.E., Abdo, Z., Bent, S.J., Shyu, C., Williams, C.J., Pierson, J.D., Forney, L.J., 2008.
Advances in the use of terminal restriction fragment length polymorphism (TRFLP)
analysis of 16S rRNA genes to characterize microbial communities. Applied Microbiology
and Biotechnology 80, 365–380.
Sessitsch, A., Weilharter, A., Gerzabek, M.H., Kirchmann, H., Kandeler, E., 2001. Microbial
population structures in soil particle size fractions of a long-term fertilizer field experiment.
Applied and Environmental Microbiology 67, 4215–4224.
Shannon, C.E., Weaver, W., 1949. The mathematical theory of communication. Urbana IL:
University of Illinois Press.
Shen, J., Zhang, L., Zhu, Y., Zhang, J., He, J., 2008. Abundance and composition of ammonia-
oxidizing bacteria and ammonia-oxidizing archaea communities of an alkaline sandy loam.
Environmental Microbiology 10, 1601–1611.
Shen, J.P., Zhang, L.M., Guo, J.F., Ray, J.L., He, J.Z., 2010. Impact of long-term fertilization
practices on the abundance and composition of soil bacterial communities in Northeast
China. Applied Soil Ecology 46, 119–124.
Shepherd, D.K., Cheryl, A.P., Gachengo, N.C., Vanlauwe, B., 2003. Rapid characterization of
organic resource quality for soil and livestock management in tropical agroecosystems
using Near-Infrared Spectroscopy. Agronomy Journal 95, 1314–1322.
Singh, B.K., Bardgett, R.D., Smith, P., Reay, D.S., 2010. Microorganisms and climate change:
terrestrial feedbacks and mitigation options. Nature Reviews Microbiology 8, 779–790.
Six, J., Bossuyt, H., Degryze, S., Denef, K., 2004. A history of research on the link between
(micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res.,
Advances in Soil Structure Research 79, 7–31.
140
Six, J., Frey, D.S., Thiet, K.R., and Batten, M.K., 2006. Bacterial and fungal contributions to
carbon sequestration in agroecosystems. Soil Science Society of America Journal 70, 555–
569.
Smalla, K., Oros-Sichler, M., Milling, A., Heuer, H., Baumgarte, S., Becker, R., Neuber, G.,
Kropf, S., Ulrich, A., Tebbe, C.C., 2007. Bacterial diversity of soils assessed by DGGE,
TRFLP and SSCP fingerprints of PCR-amplified 16S rRNA gene fragments: Do the
different methods provide similar results? Journal of Microbiological Methods 69, 470–
479.
Smith, C.J., Osborn, A.M., 2009. Advantages and limitations of quantitative PCR (QPCR)-based
approaches in microbial ecology. FEMS Microbiology Ecology 67, 6–20.
Stark, J.M., Firestone, M.K., 1995. Mechanisms for soil moisture effects on activity of nitrifying
bacteria. Applied and Environtal Microbiology 61, 218–221.
Stevenson, A., Cray, J.A., Williams, J.P., Santos, R., Sahay, R., Neuenkirchen, N., McClure, C.D.,
Grant, I.R., Houghton, J.D.R., Quinn, J.P., Timson, D.J., Patil, S.V., Singhal, R.S., Anton,
J., Dijksterhuis, J., Hocking, A.D., Lieven, B., Rangel, D.E.N., Voytek, M.A., Gunde-
Cimerman, N., Oren, A., Timmis, K.N., McGenity, T.J., Hallsworth, J.E., 2015. Is there a
common water-activity limit for the three domains of life? The ISME Journal 9,1333–1351.
Stevenson, A., Hallsworth, J.E., 2014. Water and temperature relations of soil Actinobacteria:
Water and temperature relations of Actinobacteria. Environmental Microbiology Reports
6, 744–755.
Stopnišek, N., Gubry-Rangin, C., Hofferle, Š., Nicol, G.W., Mandič-Mulec, I., Prosser, J.I., 2010.
Thaumarchaeal ammonia oxidation in an acidic forest peat soil is not influenced by
ammonium amendment. Applied and Environmental Microbiology 76, 7626–7634.
Strauss, S.L., Reardon, C.L., Mazzola, M., 2014. The response of ammonia-oxidizer activity and
community structure to fertilizer amendment of orchard soils. Soil Biology and
Biochemistry 68, 410–418.
Subbarao, G.V., Sahrawat, K.L., Nakahara, K., Rao, I.M., Ishitani, M., Hash, C.T., Kishii, M.,
Bonnett, D.G., Berry, W.L., Lata, J.C., 2013. A paradigm shift towards low-nitrifying
production systems: the role of biological nitrification inhibition (BNI). Annals of Botany
112, 297–316.
141
Szukics, U., Abell, G.C.J., Hödl, V., Mitter, B., Sessitsch, A., Hackl, E., Zechmeister-Boltenstern,
S., 2010. Nitrifiers and denitrifiers respond rapidly to changed moisture and increasing
temperature in a pristine forest soil. FEMS Microbiology Ecology 72, 395–406.
Szukics, U., Hackl, E., Zechmeister-Boltenstern, S., Sessitsch, A., 2012. Rapid and dissimilar
response of ammonia-oxidizing archaea and bacteria to nitrogen and water amendment in
two temperate forest soils. Microbiological Research 167, 103–109.
Talbot, J., M., Finzi, A.C., 2008. Differential effects of sugar maple, red oak, and hemlock tannins
on carbon and nitrogen cycling in temperate forest soils. Oecologia. 155, 583–592.
Talbot, J.M., Yelle, D.J., Nowick, J., Treseder, K.K., 2012. Litter decay rates are determined by
lignin chemistry. Biogeochemistry 108, 279–295.
Tekaign, T., Haque, I., Aduayi, E.A., 1991. Soil, plant, water, fertilizer, animal manure and
compost analysis manual. Plant science division working document 13, ILCA, Addis
Ababa, Ethiopia.
Tian, G., Brussaard, L., Kang, B.T., 1995. An index for assessing the quality of plant residues and
evaluating their effects on soil and crop in the sub-humid tropics. Applied Soil Ecology 2,
25–32.
Tisdall, J.M. 1991. Fungal hyphae and structural stability of soil. Australian Journal of Soil
Research 29, 729–743.
Todd-Brown, K.E.O., Hopkins, F.M., Kivlin, S.N., Talbot, J.M., Allison, S.D., 2012. A framework
for representing microbial decomposition in coupled climate models. Biogeochemistry
109, 19–33.
Tourna, M., Freitag, T. E., Prosser, J. I., 2010. Stable isotope probing analysis of interactions
between ammonia oxidizers. Applied and Environmental Microbiology 76, 2468–2477.
Tourna, M., Stieglmeier, M., Spang, A., Könneke, M., Schintlmeister, A., Urich, T., Engel, M.,
Schloter, M., Wagner, M., Richter, A., Schleper, C., 2011. Nitrososphaera viennensis, an
ammonia oxidizing archaeon from soil. Proceedings of the National Academy of Sciences
108, 8420–8425.
Treusch, A. H., Leininger, S., Kletzin, A., Schuster, S. C., Klenk, H. P., Schleper, C., 2005. Novel
genes for nitrite reductase and Amo-related proteins indicate a role of uncultivated
mesophilic crenarchaeota in nitrogen cycling. Environmental Microbiology 7, 1985–1995.
142
Valentine, D.L., 2007. Adaptations to energy stress dictate the ecology and evolution of the
archaea. Nature Reviews Microbiology 5, 316–323.
Vanlauwe, B., Aihou, K., Aman, S., Iwuafor, E.N.O., Tossah, B.K., Diels, J., Sanginga, N., Lyasse,
O., Merckx, R., Deckers, J., 2001b. Maize yield as affected by organic inputs and urea in
the West African moist savanna. Agronomy Journal 93, 1191–1199.
Vanlauwe, B., Bationo, A., Chianu, J., Giller, K.E., Merckx, R., Mokwunye, U., Ohiokpehai, O.,
Pypers, P., Tabo, R., Shepherd, K.D., Smaling, E.M.A., Woomer, P.L., Sanginga, N., 2010.
Integrated soil fertility management: Operational definition and consequences for
implementation and dissemination. Outlook in Agriculture 39, 17–24.
Vanlauwe, B., Gachengo, C., Shepherd, K., Barrios, E., Cadisch, G., Palm, C.A., 2005. Laboratory
validation of a resource quality-based conceptual framework for organic matter
management. Soil Science Society of America Journal, 69, 1135–1145.
Vanlauwe, B., Wendt, J., Diels, J., 2001a. Combined application of organic matter and fertilizer.
In: Tian, G., Ishida, F., Keatinge, J.D.H. (eds.), Sustaining soil fertility in west Africa. Soil
Science Society of America and American Society of Agronomy, Madison, pp. 247–279.
Verhamme, D.T., Prosser, J.I., Nicol, G.W., 2011. Ammonia concentration determines differential
growth of ammonia-oxidising archaea and bacteria in soil microcosms. The ISME Journal
5, 1067–1071.
Verkaik, E., Jongkind, A.G., Berendse, F., 2006. Short-term and long-term effects of tannins on
nitrogen mineralisation and litter decomposition in kauri (Agathis australis (D. Don)
Lindl.) forests. Plant Soil 287, 337–345.
Wang, Y., Ke, X., Wu, L., Lu, Y., 2009. Community composition of ammonia-oxidizing bacteria
and archaea in rice field soil as affected by nitrogen fertilization. Systematic and Applied
Microbiology 32, 27–36.
Wardle, D.A., Giller, K.E., 1996. The quest for a contemporary ecological dimension to soil
biology. Soil Biology and Biochemistry 28, 1549–1554.
Weisburg, W.G., Barns, S.M., Pelletier, D.A., Lane, D.J., 1991. 16S ribosomal DNA amplification
for phylogenetic study. Journal of Bacteriology 173, 697–703.
Wessén, E., Nyberg, K., Jansson, J.K., Hallin, S., 2010. Responses of bacterial and archaeal
ammonia-oxidizers to soil organic and fertilizer amendments under long-term
management. Applied Soil Ecology 45, 193–200.
143
Wessén, E., Söderström, M., Stenberg, M., Bru, D., Hellman, M., Welsh, A., Thomsen, F.,
Klemedtson, L., Philippot, L., Hallin, S., 2011. Spatial distribution of ammonia-oxidizing
bacteria and archaea across a 44-hectare farm related to ecosystem functioning. The ISME
Journal 5, 1213–1225.
World Bank, 2012. Global Food Policy Report. Washington, DC, USA.
http://www.ifpri.org/gfpr/2012/employment-agriculture
World Bank, 2012. World population prospects. United Nations, New York, USA.
Wu, Y., Lu, L., Wang, B., Lin, X., Zhu, J., Cai, Z., Yan, X., Jia, Z., 2011. Long-term field
fertilization significantly alters community structure of ammonia-oxidizing bacteria rather
than archaea in a paddy soil. Soil Science Society of America Journal 75, 1431–1439.
Xu, M., Schnorr, J., Keibler, B., Simon, H.M., 2012. Comparative analysis of 16S rRNA and amoA
genes from archaea selected with organic and inorganic amendments in enrichment culture.
Applied and Environmental Microbiology 78, 2137–2146.
Yao, H., Gao, Y., Nicol, G. W., Campbell, C. D., Prosser, J. I., Zhang, L., Han, W., Singh, B. K.,
2011. Links between ammonia oxidizer community structure, abundance, and nitrification
potential in acidic soils. Applied and Environmental Microbiology 77, 4618–4625.
Zhalnina, K., Dörr de Quadros, P., A. O. Camargo, F., Triplett, E.W., 2012. Drivers of archaeal
ammonia-oxidizing communities in soil. Terrestrial Microbiology 3, 1–9.
Zhang, L., Hu, H., Shen, J., He, J., 2012. Ammonia-oxidizing archaea have more important role
than ammonia-oxidizing bacteria in ammonia oxidationof strongly acidic soils. The ISME
Journal 6, 1032–1045.
Zhang, L.-M., Offre, P.R., He, J.-Z., Verhamme, D.T., Nicol, G.W., Prosser, J.I., 2010.
Autotrophic ammonia oxidation by soil thaumarchaea. Proceedings of the National
Academy of Sciences 107, 17240–17245.
Zhang, X., Liu, W., Schloter, M., Zhang, G., Chen, Q., Huang, J., Li, L., Elser, J.J., Han, X., 2013.
Response of the abundance of key soil microbial nitrogen-cycling genes to multi-factorial
global changes. PLoS ONE 8, 1–10.
Zinn, Y.L., Lal, R., Resck, D.V.S., 2005. Changes in soil organic carbon stocks under agriculture
in Brazil. Soil and Tillage Research 84, 28–40.
144
Zheng, X., Wang, M., Wang, Y., Shen, R., Gou, J., Li, J., Jin, J., Li, L., 2000. Impacts of soil
moisture on nitrous oxide emission from croplands: a case study on the rice-based agro-
ecosystem in Southeast China. Chemosphere - Global Change Science 2, 207–224.